Prepare dataframe
Reading data
Data inclusion criteria: variable name “MOL”= “PREGABALIN”,“GABAPENTIN”,“GABAPENTIN ENACARBIL”,“MIROGABALIN”
library(readxl)
library(dplyr)
# data <- read.csv("D:/MIDAS ADHD/midas_adhd/adhd/MIDAS 2019.csv")
# data
getwd()
## [1] "D:/R/midas gaba"
setwd("D:/R/midas gaba")
gaba<-subset(read.csv("D:/R/midas benzo/raw/gaba_extract.csv"),MOL=="PREGABALIN"|
MOL=="GABAPENTIN"|
MOL=="GABAPENTIN ENACARBIL"|
MOL=="MIROGABALIN")
Drug inspection
Inspect data for combination product with more than one active molecule (Gabapentinoids)
The remaining products that may contain more than one drug of interest are: INDIA TOTAL SALES RETAIL A.N.PHARMACIA N3A ANTI-EPILEPTICS NEURO-P
* only one record per active ingredient
library(dplyr)
library(stringr)
data.1.nodup<-gaba
#Check for combination product i.e. other than drug name, all numbers are the same
exam2<-data.1.nodup[duplicated(data.1.nodup[,-(1:10)]) | duplicated(data.1.nodup[,-(1:10)], fromLast = TRUE),]
exam2
gaba$Class<-"Gabapentinoids"
We assume that the DDD units provided only refer to that specific drug ingredient in the data row
Keep SU and DDD data
master<-gaba
Separate data into with DDD units and without DDD units (INTWHODDDDESC NOT ASSIGNED)
- For products without DDD data, the conversion ratio is based on standard units sold, WHO-ATC defined daily dose, and product strength (539 observations)
- For products without drug strength AND DDD, we will impute the conversion ratio based on the most commonly used strength
Two pharmacists have assigned strength of products without DDD units for conversion SU units.
667 observations were read from the conversion file for gabapentinoids since a product may have more than one strength. Median imputation was conducted for products with more than one identified strength. 470/539 observations with strengths identified after median imputation.
- Liquid formulations were removed (15/539 unique metadata combination in missing DDD file)
- Strength information of 54/539 country-product combinations cannot be identified
library(dplyr)
#Identify products that need conversion from SU to INTDDD
missing_DDD<-subset(master,INTWHODDDDESC=="INTWHODDD DESC NOT ASSIGNED")
with_DDD<-subset(master,INTWHODDDDESC!="INTWHODDD DESC NOT ASSIGNED")
# #Export list for conversion factor identification
# miss_DDD<-unique(missing_DDD[c(1:8)])
# write.csv(miss_DDD,"D:/R/midas benzo/dosage_convert.csv")
#Daniel and Andrew has done the conversion
convert <- read.table(
"D:/R/midas benzo/convert_20221114_removed.txt",
sep="\t", header=TRUE)
convert2<-subset(convert, !is.na(convert$Strength.of.BZD.or.GABA) & (MOL=="PREGABALIN"|
MOL=="GABAPENTIN"|
MOL=="GABAPENTIN ENACARBIL"|
MOL=="MIROGABALIN"))
convert_missing<-subset(convert, is.na(convert$Strength.of.BZD.or.GABA))
convert_medianimpute_strength <- convert2[c(1:10)] %>% group_by (CTY,SEC, MNF, ATC3,INTPRD,NFC123,INTWHODDDDESC,MOL,INTWHODDDDESC_2) %>% mutate(median=median(Strength.of.BZD.or.GABA, na.rm = TRUE))
convert_medianimpute_strength2<-unique(convert_medianimpute_strength[c(1:8,10,11)])
#left join convert to data
missing_DDD_convert <- left_join(missing_DDD,convert_medianimpute_strength2,by = c("CTY","SEC","MNF","ATC3","INTPRD","NFC123",
"INTWHODDDDESC","MOL") )
write.csv(missing_DDD,"D:/R/midas gaba/withoutddd_539.csv")
missing_DDD_remain<-subset(missing_DDD_convert, !is.na(median) & Class=="Gabapentinoids")
missing_DDD_agg<-missing_DDD %>% group_by (CTY, SEC,MNF,ATC3,INTPRD,NFC123,INTWHODDDDESC,MOL,X_TYPE_,X_FREQ_,Class) %>%
summarise_at(vars(1:857),sum)
Aggregate separately for DDD and missing DDD dataframes
DDD
library(tidyr)
library(zoo)
library(dplyr)
ddd <-with_DDD
## Remove columns of other sales data e.g. LC, LCD, USD, CU, SU
ddd.2 <- ddd[-c(9:10)]%>%select(-contains(c("LC_MNF","LCD_MNF","USD_MNF","CU_MTH","SU_MTH")))
ddd.3 <- pivot_longer(ddd.2, cols=9:151,
names_to = "Month", values_to = "DDD")
## Format date
ddd.3$Month<-str_remove(ddd.3$Month, "INTDDD_MTH_")
ddd.3<-ddd.3 %>% separate(Month, c("Month","Year"),
sep = "([_])")
ddd.3$Year <- paste0("20", ddd.3$Year, sep = "")
ddd.3$Year<-as.numeric(ddd.3$Year)
ddd.3$Month<-as.numeric(ddd.3$Month)
ddd.3$Date <- as.yearmon(paste(ddd.3$Year,
ddd.3$Month), "%Y %m")
ddd.4<-ddd.3[-c(10)]
ddd.4$DDD[is.na(ddd.4$DDD)]<-0
Without DDD
Check formula: DDD units = SU units *Strength/INTWHODDDDESC
library(tidyr)
library(zoo)
library(dplyr)
noddd <-missing_DDD_remain
## Remove columns of other sales data e.g. LC, LCD, USD, CU, SU
noddd.2 <- noddd[-c(9:10)]%>%select(-contains(c("LC_MNF","LCD_MNF","USD_MNF","CU_MTH","INTDDD_MTH")))
noddd.3 <- pivot_longer(noddd.2, cols=9:151,
names_to = "Month", values_to = "SU")
## Calculate DDD
noddd.3$DDD<-noddd.3$SU*noddd.3$median/noddd.3$INTWHODDDDESC_2
## Format date
noddd.3$Month<-str_remove(noddd.3$Month, "SU_MTH_")
noddd.3<-noddd.3 %>% separate(Month, c("Month","Year"),
sep = "([_])")
noddd.3$Year <- paste0("20", noddd.3$Year, sep = "")
noddd.3$Year<-as.numeric(noddd.3$Year)
noddd.3$Month<-as.numeric(noddd.3$Month)
noddd.3$Date <- as.yearmon(paste(noddd.3$Year,
noddd.3$Month), "%Y %m")
noddd.4<-noddd.3
noddd.4$DDD[is.na(noddd.4$DDD)]<-0
Aggregate the consumption data by country, drug name, date
ddd.5<-ddd.4
noddd.5<-noddd.4[-c(10,11,12,14)]
bind<-rbind(ddd.5,noddd.5)
aggregate<-bind[-c(2:7)]
#Step 1. Clean city names
aggregate$CTY = sub(pattern = "(RETAIL)|(COMBINED)|(COMBINE)|(COMBIN)|(COMBI)|(RET)|(R.MUTUALES)|(HOSPITAL)|(TOTAL SALES)",
replacement = "", x = aggregate$CTY, perl = TRUE)
aggregate.2<-aggregate %>% group_by (CTY, MOL,Class,Year, Date) %>%
summarise_at(vars(1),sum, na.rm = TRUE)
Merge rx data with UN population data
We excluded data where Country==“FRENCH WEST AFRICA” or Country==“CENTRAL AMERICA” as national level data was not available. The included countries/regions were divided into the following areas: Northern America, Central and South America, Northern Europe, Eastern Europe, Southern Europe, Western Europe, Oceania, Central Asia, Eastern Asia, South-eastern Asia, Southern Asia, Western Asia, Northern Africa, and Southern Africa, based on their geographical regions according to United Nations (UN)’ “Standard Country or Area Codes for Statistical Use”.
library(readxl)
library(dplyr)
#population size from UN====
pop <- read_excel(path = "D:/R/midas adhd/ref data/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx",
sheet = "ESTIMATES",
range = "B17:DE18122")
pop.2<-subset(pop, Type=="Country/Area")
pop.4<-pop.2[-c(1,3:63,77:108)]
names(pop.4)[names(pop.4) == 'Region, subregion, country or area *'] <- 'Country'
pop.5 <- pivot_longer(pop.4, cols=2:14,
names_to = "Year", values_to = "Population")
pop.5$Country <- toupper(pop.5$Country)
names(aggregate.2)[names(aggregate.2) == 'CTY'] <- 'Country'
aggregate.2$Country <-trimws(aggregate.2$Country)
'%ni%' <- Negate('%in%')
ddd.set.2<- subset(aggregate.2, !Country=="C. AMERICA"&
!Country=="FR. W. AFRICA"&
!Country=="FRENCH WEST AFRICA"&
!Country=="CENTRAL AMERICA")
pop.7<-pop.5
rename <- pop.7 %>%filter(Country %ni% ddd.set.2$Country)
rename<-unique(rename$Country)
ddd.set.2[ddd.set.2$Country == "CZECH", "Country"] <- "CZECH REPUBLIC"
ddd.set.2[ddd.set.2$Country == "NETHERLNDS", "Country"] <- "NETHERLANDS"
ddd.set.2[ddd.set.2$Country == "RUSSIAN FED.", "Country"] <- "RUSSIA"
ddd.set.2[ddd.set.2$Country == "TURKEY", "Country"] <- "TÜRKİYE"
ddd.set.2[ddd.set.2$Country == "UAE", "Country"] <- "UNITED ARAB EMIRATES"
ddd.set.2[ddd.set.2$Country == "UK", "Country"] <- "UNITED KINGDOM"
ddd.set.2[ddd.set.2$Country == "USA", "Country"] <- "UNITED STATES"
ddd.set.2[ddd.set.2$Country == "US", "Country"] <- "UNITED STATES"
ddd.set.2[ddd.set.2$Country == "S. AFRICA", "Country"] <- "SOUTH AFRICA"
ddd.set.2[ddd.set.2$Country == "BOSNIA", "Country"] <- "BOSNIA AND HERZEGOVINA"
ddd.set.2[ddd.set.2$Country == "KOREA", "Country"] <- "SOUTH KOREA"
pop.7<-pop.5
pop.7[pop.7$Country == "CZECHIA", "Country"] <- "CZECH REPUBLIC"
pop.7[pop.7$Country == "REPUBLIC OF KOREA", "Country"] <- "SOUTH KOREA"
pop.7[pop.7$Country == "RUSSIAN FEDERATION", "Country"] <- "RUSSIA"
pop.7[pop.7$Country == "TURKEY", "Country"] <- "TÜRKİYE"
pop.7[pop.7$Country == "CHINA, TAIWAN PROVINCE OF CHINA", "Country"] <- "TAIWAN"
pop.7[pop.7$Country == "VENEZUELA (BOLIVARIAN REPUBLIC OF)", "Country"] <- "VENEZUELA"
pop.7[pop.7$Country == "UNITED STATES OF AMERICA", "Country"] <- "UNITED STATES"
pop.8<-as.data.frame(pop.7)
ddd.set.2$Year<-as.numeric(ddd.set.2$Year)
pop.8$Year<-as.numeric(pop.8$Year)
ddd.set.3.1<-ddd.set.2
ddd.set.3 <- left_join(ddd.set.3.1,pop.8,by = c("Country","Year") )
## Merge data with Region categorisation====
Region <- read_excel(path = "D:/R/midas adhd/ref data/UNSD — Methodology.xlsx")
Region.2<-Region[c(6,9,16)]
names(Region.2)[names(Region.2) == 'Country or Area'] <- 'Country'
Region.2$Country <- toupper(Region.2$Country)
rename <- ddd.set.3%>%filter(Country %ni% Region.2$Country)
rename<-unique(rename$Country)
Region.2[Region.2$Country == "CZECHIA", "Country"] <- "CZECH REPUBLIC"
Region.2[Region.2$Country == "REPUBLIC OF KOREA", "Country"] <- "SOUTH KOREA"
Region.2[Region.2$Country == "RUSSIAN FEDERATION", "Country"] <- "RUSSIA"
Region.2[Region.2$Country == "CHINA, TAIWAN PROVINCE OF CHINA", "Country"] <- "TAIWAN"
Region.2[Region.2$Country == "TURKEY", "Country"] <- "TÜRKİYE"
Region.2[Region.2$Country == "VENEZUELA (BOLIVARIAN REPUBLIC OF)", "Country"] <- "VENEZUELA"
Region.2[Region.2$Country == "UNITED STATES OF AMERICA", "Country"] <- "UNITED STATES"
Region.2[Region.2$Country == "UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND", "Country"] <- "UNITED KINGDOM"
Region.3<-Region.2%>%filter(Country %in% ddd.set.3$Country)
ddd.set.4 <- left_join(ddd.set.3,Region.3,by = c("Country") )
names(ddd.set.4)[names(ddd.set.4) == 'Sub-region Name'] <- 'Region'
ddd.set.4[ddd.set.4$Country == "TAIWAN", "Region"] <- "Eastern Asia"
ddd.set.5<-ddd.set.4[-c(9)]
## 1.9 Dave comment: change Latin America
ddd.set.5[ddd.set.5$Region == "Latin America and the Caribbean", "Region"] <- "Central and South America and the Caribbean"
ddd.set.5[ddd.set.5$Region == "Sub-Saharan Africa", "Region"] <- "Southern Africa"
population<-pop.8
population$Population<-as.numeric(population$Population)
options(scipen=999)
Final analytic dataset
- Data from 2007 and 2019 were removed due to missing months
- DDD data were aggregated by Drug, Country, Year
- DDD data were combined with population data
- zeros were added to countries were there were no record in that year
- Outcome metric = DDD per thousand population per day (DDD/(population in thousand*365.25))
- Final cleaned analytic dataset is cleaned as below with variables specification:
library(dplyr)
cty.all<-ddd.set.5
names(cty.all)[names(cty.all) == 'MOL'] <- 'Drug'
## create one df of monthly data just in case
cty.all.3<-cty.all[-c(7)] %>% group_by (Year,Date,Class,Drug, Country, Region) %>%
summarise_at(vars(1),sum, na.rm = TRUE)
## numerator
cty.ddd.2<-cty.all[-c(5,7)] %>% group_by (Year,Class,Drug, Country, Region) %>%
summarise_at(vars(1),sum, na.rm = TRUE)
##denominator
cty.pop<-population
#Aggregate benzos and gaba
#gaba
new_gaba<-subset(cty.ddd.2)
new_gaba<-new_gaba[-c(3)]
new_gaba<-new_gaba %>% group_by (Year,Country, Region,Class) %>%
summarise_at(vars(1),sum, na.rm = TRUE)
new_gaba$Drug<-"Gabapentinoids"
new_gaba$DDD[is.na(new_gaba$DDD)]<-0
cty.ddd.2<-rbind(cty.ddd.2,new_gaba)
#Add zeros
Drug<-sort(rep(c("Gabapentinoids","MIROGABALIN","PREGABALIN","GABAPENTIN","GABAPENTIN ENACARBIL"),715))
Country<-(rep(unique(cty.ddd.2$Country),11))
Year<-sort(rep(c(2008:2018),65))
merge_zero <- data.frame(Country, Year)
merge_zero2<-do.call("rbind", replicate(5, merge_zero, simplify = FALSE))
merge_zero2$Drug<-Drug
cty.ddd.3 <- right_join(x=cty.ddd.2,y=merge_zero2,
by=c("Year","Country","Drug"))
cty.ddd.4 <- left_join(x=cty.ddd.3,y=cty.pop,
by=c("Year","Country"))
cty.ddd.5 <- distinct(left_join(x=cty.ddd.4[-c(2,5)],y=ddd.set.5[c(1,3,8)],
by=c("Country")))
cty.ddd.5$DDD[is.na(cty.ddd.5$DDD)]<-0
#Divide
cty.ddd.5$DDDPTPD <-(cty.ddd.5$DDD/cty.ddd.5$Population)/365.25
cty.ddd.5$Drug<-str_to_title(cty.ddd.5$Drug)
analy<-subset(cty.ddd.5,Year!=2007 & Year!=2019 & Drug!="Vigbatrin"&Drug!="Mirogabalin")
library(DT)
datatable(analy, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
str(analy,give.attr=FALSE)
## grouped_df [2,860 x 8] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Year : num [1:2860] 2008 2008 2008 2008 2008 ...
## $ Drug : chr [1:2860] "Gabapentin" "Gabapentin" "Gabapentin" "Gabapentin" ...
## $ Country : chr [1:2860] "ALGERIA" "ARGENTINA" "AUSTRALIA" "AUSTRIA" ...
## $ DDD : num [1:2860] 0 1532866 5911624 4399525 16780 ...
## $ Population: num [1:2860] 34731 40080 21332 8342 9453 ...
## $ Class : chr [1:2860] "Gabapentinoids" "Gabapentinoids" "Gabapentinoids" "Gabapentinoids" ...
## $ Region : chr [1:2860] "Northern Africa" "Central and South America and the Caribbean" "Australia and New Zealand" "Western Europe" ...
## $ DDDPTPD : num [1:2860] 0 0.10471 0.75872 1.44401 0.00486 ...
write.csv(analy,"D:/R/midas gaba/R1/Output_1_Analytic_dataset.csv")
Main Analysis: DDD/TID and trends
Overview of gabapentinoids consumption by Drug, Country, and Year
*MIROGABALIN is only available in Japan in 2019 which is not covered in our analysis
library(ggplot2)
library(RColorBrewer)
new_set.2<-analy
stacked<-subset(new_set.2, Drug!="Gabapentinoids" )
stacked$Country<-toupper(stacked$Country)
stacked[
order( stacked[,2] ,stacked[,3]),
]
stacked$Drug <- factor(stacked$Drug)
stacked$Region <- factor(stacked$Region, levels =
c("Global","Northern America","Central and South America and the Caribbean",
"Northern Europe", "Eastern Europe","Southern Europe", "Western Europe",
"Oceania" ,
"Eastern Asia" ,
"South-eastern Asia","Southern Asia" ,
"Western Asia","Central Asia",
"Northern Africa", "Southern Africa"
))
a<-ggplot(stacked[order(stacked$Drug), ], aes(x = str_to_title(Country), y = DDDPTPD*10, fill = Drug)) +
geom_col(alpha = 0.8, width = 0.9) +
scale_y_continuous(expand = c(0, 0.1)) +
scale_x_discrete(limits=rev)+
coord_flip() +
facet_grid(rows = vars(Region), cols = vars(Year),
scales = "free_y", switch = "y",
space = "free_y",
labeller = label_wrap_gen(width=20)) +
labs(
title = "Gabapentinoids consumption from 2008 to 2018",
subtitle = "in 65 countries",
caption = "Figure",
y = "Defined Daily Dose per 10000 inhabitants per day"
) +
theme_minimal() +
scale_fill_manual(values = c("#EC2049", "#DCEDC2", "#2F9599"))+
# scale_fill_brewer(palette = "Dark2")+
theme(
plot.margin = margin(0.5, 0.5, 0.5, 0.5, unit = "cm"),
plot.title = element_text(size = 15, face = "bold"),
panel.spacing.x = unit(1, "lines"),
# strip.text.y = element_text(angle = 270, face = "bold"),
strip.text.y.left = element_text(angle = 0,face = "bold"),
strip.placement = "outside",
axis.title.x = element_text(margin = margin(t = 0.5, b = 0.5, unit = "cm")),
axis.title.y = element_blank(),
axis.text = element_text(size = 12),
axis.text.x=element_text(angle=270),
legend.position = "top",
panel.grid.major.y = element_blank()
)
a
ggsave(filename="Figure 1_Consumption by Year Country Drug.png", plot=a, height =13, width=16,device="png",
path="D:/R/midas gaba/R1/figures",
dpi=800)
Calculate CIs of consumption in DDD/TID per year
The function pois.approx from R package epitools is used to calculation the Confidence intervals for Poisson rates with normal approximation
library(epitools)
CIs<-pois.approx(new_set.2$DDD, pt = new_set.2$Population*365.25, conf.level = 0.95)
pool.new_set<-cbind(new_set.2,CIs)
pool.new_set.2<-subset(pool.new_set)
pool.new_set.2$DDD<-round(pool.new_set.2$DDD, digits = 5)
write.csv(pool.new_set.2,"D:/R/midas gaba/R1/Output_2_Gaba_consumption_CI.csv")
writexl::write_xlsx(pool.new_set.2,"D:/R/midas gaba/R1/Output_2_Gaba_consumption_CI.xlsx")
Meta-analysis of DDD/TID by Year
When DDD=0 or extremely small, Country data wont be counted as number of studies in the meta analysis
Drug indices: [1] “Gabapentin”, [2] “Gabapentin Enacarbil”, [3] “Pregabalin”, [4]“Gabapentinoids”
options(width=800)
library(Rcpp)
library(meta)
library(data.table)
library(dplyr)
set_subzero<-subset(analy, Drug!="MIROGABALIN")
set_subzero$DDD_dum=set_subzero$DDD
set_subzero$DDD_dum[set_subzero$DDD==0]<-0.00001
CIs<-pois.approx(set_subzero$DDD_dum, set_subzero$Population*365.25, conf.level = 0.95)
meta.gaba<-cbind(set_subzero,CIs)
meta.gaba$Region <- factor(meta.gaba$Region, levels =
c("Northern America","Central and South America and the Caribbean",
"Northern Europe", "Eastern Europe","Southern Europe", "Western Europe",
"Australia and New Zealand" ,
"Eastern Asia" , "Central Asia",
"South-eastern Asia","Southern Asia" ,
"Western Asia",
"Northern Africa","Southern Africa"
))
meta <- function(rho, iseed){
meta.gaba_1<- subset(meta.gaba, Year==rho & Drug==iseed)
m1_var<-metagen(log(meta.gaba_1$rate),
lower = log(meta.gaba_1$lower),
upper = log(meta.gaba_1$upper),
studlab = meta.gaba_1$Country,
sm = "IRLN", method.tau = "DL",
comb.fixed = TRUE,
byvar = meta.gaba_1$Region)
print(c(rho, iseed))
print(summary(m1_var), digits=4)
est.random<-c("Year", "DDD/TID", "DDD/TID - lower","DDD/TID - upper")
est.random$Year<-rho
est.random$Drug<-iseed
est.random$`DDD/TID`<-exp(summary(m1_var)$TE.random)
est.random$`DDD/TID - lower`<-exp(summary(m1_var)$lower.random)
est.random$`DDD/TID - upper`<-exp(summary(m1_var)$upper.random)
est.by.random<-c("Year", "DDD/TID", "DDD/TID - lower","DDD/TID - upper")
est.by.random$Year<-rho
est.by.random$Drug<-iseed
est.by.random$`DDD/TID`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$TE)))))
est.by.random$`DDD/TID - lower`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$lower)))))
est.by.random$`DDD/TID - upper`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$upper)))))
return(c(est.random,est.by.random))
}
datin <- expand.grid(rho = unique(meta.gaba$Year), iseed = unique(meta.gaba$Drug))
i <- 1:nrow(datin)
datout <- with(datin,
lapply(i, function(j){meta(rho[j], iseed[j])}))
## [1] 2008 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1047 [0.1045; 0.1049] 0.2 1.7 Central and South America and the Caribbean
## AUSTRALIA 0.7587 [0.7581; 0.7593] 0.7 1.7 Australia and New Zealand
## AUSTRIA 1.4440 [1.4427; 1.4454] 0.5 1.7 Western Europe
## BELARUS 0.0049 [0.0048; 0.0049] 0.0 1.7 Eastern Europe
## BELGIUM 0.3490 [0.3484; 0.3495] 0.2 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0522 [0.0522; 0.0523] 0.5 1.7 Central and South America and the Caribbean
## BULGARIA 0.0867 [0.0863; 0.0870] 0.0 1.7 Eastern Europe
## CANADA 2.4878 [2.4870; 2.4887] 3.7 1.7 Northern America
## CHILE 0.0425 [0.0423; 0.0426] 0.0 1.7 Central and South America and the Caribbean
## CHINA 0.0009 [0.0009; 0.0009] 0.1 1.7 Eastern Asia
## COLOMBIA 0.0340 [0.0339; 0.0341] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.2926 [0.2918; 0.2935] 0.1 1.7 Southern Europe
## CZECH REPUBLIC 0.7153 [0.7145; 0.7162] 0.3 1.7 Eastern Europe
## ECUADOR 0.0727 [0.0725; 0.0730] 0.0 1.7 Central and South America and the Caribbean
## EGYPT 0.0759 [0.0758; 0.0760] 0.3 1.7 Northern Africa
## ESTONIA 0.0754 [0.0746; 0.0761] 0.0 1.7 Northern Europe
## FINLAND 0.9593 [0.9579; 0.9607] 0.2 1.7 Northern Europe
## FRANCE 1.3793 [1.3788; 1.3798] 3.9 1.7 Western Europe
## GERMANY 1.2294 [1.2290; 1.2298] 4.5 1.7 Western Europe
## GREECE 0.8335 [0.8326; 0.8344] 0.4 1.7 Southern Europe
## HUNGARY 0.2259 [0.2254; 0.2264] 0.1 1.7 Eastern Europe
## INDIA 0.0320 [0.0319; 0.0320] 1.7 1.7 Southern Asia
## IRELAND 0.9193 [0.9178; 0.9207] 0.2 1.7 Northern Europe
## ITALY 0.5709 [0.5706; 0.5712] 1.5 1.7 Southern Europe
## JAPAN 0.1016 [0.1015; 0.1017] 0.6 1.7 Eastern Asia
## JORDAN 0.0755 [0.0751; 0.0758] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0298 [0.0294; 0.0301] 0.0 1.7 Western Asia
## LATVIA 0.3321 [0.3308; 0.3334] 0.0 1.7 Northern Europe
## LEBANON 0.2416 [0.2409; 0.2424] 0.1 1.7 Western Asia
## LITHUANIA 0.2187 [0.2179; 0.2195] 0.0 1.7 Northern Europe
## LUXEMBOURG 0.8551 [0.8508; 0.8595] 0.0 1.7 Western Europe
## MEXICO 0.0713 [0.0712; 0.0713] 0.4 1.7 Central and South America and the Caribbean
## MOROCCO 0.0074 [0.0073; 0.0074] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.9749 [0.9734; 0.9765] 0.2 1.7 Australia and New Zealand
## NORWAY 1.1055 [1.1040; 1.1071] 0.2 1.7 Northern Europe
## PAKISTAN 0.0542 [0.0542; 0.0543] 0.4 1.7 Southern Asia
## PERU 0.0242 [0.0241; 0.0243] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0260 [0.0260; 0.0261] 0.1 1.7 South-eastern Asia
## POLAND 0.1470 [0.1468; 0.1472] 0.3 1.7 Eastern Europe
## PORTUGAL 1.3098 [1.3087; 1.3109] 0.6 1.7 Southern Europe
## PUERTO RICO 3.7459 [3.7426; 3.7492] 0.6 1.7 Central and South America and the Caribbean
## ROMANIA 0.0597 [0.0595; 0.0599] 0.1 1.7 Eastern Europe
## RUSSIA 0.0152 [0.0152; 0.0152] 0.1 1.7 Eastern Europe
## SAUDI ARABIA 0.1059 [0.1057; 0.1061] 0.1 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.5982 [0.5971; 0.5993] 0.1 1.7 Eastern Europe
## SLOVENIA 0.4548 [0.4533; 0.4564] 0.0 1.7 Southern Europe
## SOUTH AFRICA 0.0515 [0.0514; 0.0516] 0.1 1.7 Southern Africa
## SOUTH KOREA 0.6427 [0.6424; 0.6431] 1.4 1.7 Eastern Asia
## SPAIN 1.7859 [1.7853; 1.7866] 3.7 1.7 Southern Europe
## SWEDEN 1.2074 [1.2063; 1.2086] 0.5 1.7 Northern Europe
## SWITZERLAND 0.6300 [0.6291; 0.6309] 0.2 1.7 Western Europe
## TAIWAN 0.1750 [0.1747; 0.1753] 0.2 1.7 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0646 [0.0643; 0.0648] 0.0 1.7 Northern Africa
## TÜRKIYE 1.1817 [1.1813; 1.1821] 3.8 1.7 Western Asia
## UNITED ARAB EMIRATES 0.1001 [0.0997; 0.1005] 0.0 1.7 Western Asia
## UNITED KINGDOM 1.6870 [1.6864; 1.6875] 4.7 1.7 Northern Europe
## UNITED STATES 4.4760 [4.4756; 4.4764] 61.3 1.7 Northern America
## URUGUAY 0.1957 [0.1949; 0.1964] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.4496 [0.4492; 0.4500] 0.6 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 59
##
## rate 95%-CI z p-value
## Common effect model 2.3447 [2.3446; 2.3449] 24236.81 0
## Random effects model 0.2084 [0.1451; 0.2996] -8.48 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.0195 [1.5801; 5.1102]; tau = 1.4211 [1.2570; 2.2606]
## I^2 = 100.0%; H = 4154.05
##
## Test of heterogeneity:
## Q d.f. p-value
## 1000855860.37 58 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 4.3273 [4.3269; 4.3276] 9848134.09 100.0%
## Region = Central and South America and t ... 10 0.2870 [0.2869; 0.2871] 57037395.75 100.0%
## Region = Northern Europe 8 1.5146 [1.5141; 1.5150] 3503305.53 100.0%
## Region = Eastern Europe 8 0.2359 [0.2358; 0.2361] 12165172.49 100.0%
## Region = Southern Europe 6 1.2249 [1.2245; 1.2252] 13237522.89 100.0%
## Region = Western Europe 6 1.2517 [1.2514; 1.2520] 3487176.91 100.0%
## Region = Australia and New Zealand 2 0.7987 [0.7981; 0.7993] 76160.50 100.0%
## Region = Eastern Asia 4 0.3027 [0.3026; 0.3029] 28145086.76 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0260 [0.0260; 0.0261] 0.00 --
## Region = Southern Asia 2 0.0354 [0.0354; 0.0355] 764277.08 100.0%
## Region = Western Asia 6 1.0335 [1.0331; 1.0339] 9645206.33 100.0%
## Region = Northern Africa 3 0.0691 [0.0691; 0.0692] 445938.84 100.0%
## Region = Southern Africa 1 0.0515 [0.0514; 0.0516] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 862500483.20 12 0
## Within groups 138355377.17 46 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.3370 [1.8767; 5.9336] 0.1725 0.4153
## Region = Central and South America and t ... 10 0.1125 [0.0344; 0.3679] 3.6518 1.9110
## Region = Northern Europe 8 0.5683 [0.4158; 0.7767] 0.2033 0.4509
## Region = Eastern Europe 8 0.0927 [0.0360; 0.2386] 1.8636 1.3651
## Region = Southern Europe 6 0.7274 [0.4290; 1.2333] 0.4354 0.6598
## Region = Western Europe 6 0.8787 [0.7024; 1.0993] 0.0784 0.2799
## Region = Australia and New Zealand 2 0.8601 [0.6727; 1.0996] 0.0314 0.1773
## Region = Eastern Asia 4 0.0572 [0.0107; 0.3072] 2.9416 1.7151
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0260 [0.0260; 0.0261] -- --
## Region = Southern Asia 2 0.0416 [0.0248; 0.0699] 0.1397 0.3737
## Region = Western Asia 6 0.1376 [0.0374; 0.5071] 2.6562 1.6298
## Region = Northern Africa 3 0.0331 [0.0124; 0.0883] 0.7542 0.8685
## Region = Southern Africa 1 0.0515 [0.0514; 0.0516] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 211324.42 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1194 [0.1193; 0.1196] 0.2 1.7 Central and South America and the Caribbean
## AUSTRALIA 0.7770 [0.7764; 0.7776] 0.7 1.7 Australia and New Zealand
## AUSTRIA 1.5762 [1.5748; 1.5776] 0.5 1.7 Western Europe
## BELARUS 0.0093 [0.0092; 0.0094] 0.0 1.7 Eastern Europe
## BELGIUM 0.3669 [0.3663; 0.3675] 0.2 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0590 [0.0590; 0.0591] 0.5 1.7 Central and South America and the Caribbean
## BULGARIA 0.1204 [0.1200; 0.1208] 0.0 1.7 Eastern Europe
## CANADA 2.6364 [2.6355; 2.6373] 3.5 1.7 Northern America
## CHILE 0.0335 [0.0333; 0.0336] 0.0 1.7 Central and South America and the Caribbean
## CHINA 0.0020 [0.0020; 0.0020] 0.1 1.7 Eastern Asia
## COLOMBIA 0.0287 [0.0286; 0.0288] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.2979 [0.2971; 0.2988] 0.1 1.7 Southern Europe
## CZECH REPUBLIC 0.8909 [0.8900; 0.8918] 0.4 1.7 Eastern Europe
## ECUADOR 0.0696 [0.0693; 0.0698] 0.0 1.7 Central and South America and the Caribbean
## EGYPT 0.0833 [0.0832; 0.0834] 0.3 1.7 Northern Africa
## ESTONIA 0.1091 [0.1081; 0.1100] 0.0 1.7 Northern Europe
## FINLAND 0.9637 [0.9623; 0.9650] 0.2 1.7 Northern Europe
## FRANCE 1.2998 [1.2993; 1.3003] 3.2 1.7 Western Europe
## GERMANY 1.3422 [1.3418; 1.3426] 4.3 1.7 Western Europe
## GREECE 0.7983 [0.7974; 0.7991] 0.3 1.7 Southern Europe
## HUNGARY 0.2731 [0.2725; 0.2736] 0.1 1.7 Eastern Europe
## INDIA 0.0343 [0.0343; 0.0343] 1.7 1.7 Southern Asia
## IRELAND 0.9042 [0.9028; 0.9057] 0.2 1.7 Northern Europe
## ITALY 0.5370 [0.5367; 0.5373] 1.3 1.7 Southern Europe
## JAPAN 0.1478 [0.1477; 0.1479] 0.8 1.7 Eastern Asia
## JORDAN 0.0886 [0.0882; 0.0889] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0266 [0.0263; 0.0269] 0.0 1.7 Western Asia
## LATVIA 0.3719 [0.3705; 0.3732] 0.0 1.7 Northern Europe
## LEBANON 0.3655 [0.3646; 0.3664] 0.1 1.7 Western Asia
## LITHUANIA 0.2225 [0.2216; 0.2234] 0.0 1.7 Northern Europe
## LUXEMBOURG 0.7788 [0.7747; 0.7829] 0.0 1.7 Western Europe
## MEXICO 0.0705 [0.0704; 0.0706] 0.3 1.7 Central and South America and the Caribbean
## MOROCCO 0.0051 [0.0050; 0.0051] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 1.1650 [1.1633; 1.1667] 0.2 1.7 Australia and New Zealand
## NORWAY 1.4251 [1.4233; 1.4268] 0.3 1.7 Northern Europe
## PAKISTAN 0.0466 [0.0465; 0.0466] 0.3 1.7 Southern Asia
## PERU 0.0173 [0.0173; 0.0174] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0266 [0.0265; 0.0266] 0.1 1.7 South-eastern Asia
## POLAND 0.1674 [0.1672; 0.1676] 0.3 1.7 Eastern Europe
## PORTUGAL 1.1282 [1.1271; 1.1292] 0.5 1.7 Southern Europe
## PUERTO RICO 4.7038 [4.7000; 4.7075] 0.7 1.7 Central and South America and the Caribbean
## ROMANIA 0.1543 [0.1540; 0.1546] 0.1 1.7 Eastern Europe
## RUSSIA 0.0166 [0.0166; 0.0166] 0.1 1.7 Eastern Europe
## SAUDI ARABIA 0.1008 [0.1006; 0.1010] 0.1 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.7215 [0.7203; 0.7227] 0.2 1.7 Eastern Europe
## SLOVENIA 0.4132 [0.4117; 0.4146] 0.0 1.7 Southern Europe
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.7 Southern Africa
## SOUTH KOREA 0.6597 [0.6593; 0.6601] 1.3 1.7 Eastern Asia
## SPAIN 1.8188 [1.8181; 1.8194] 3.4 1.7 Southern Europe
## SWEDEN 1.2153 [1.2141; 1.2165] 0.4 1.7 Northern Europe
## SWITZERLAND 0.5953 [0.5944; 0.5962] 0.2 1.7 Western Europe
## TAIWAN 0.1779 [0.1776; 0.1781] 0.2 1.7 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0831 [0.0828; 0.0834] 0.0 1.7 Northern Africa
## TÜRKIYE 1.4377 [1.4372; 1.4381] 4.1 1.7 Western Asia
## UNITED ARAB EMIRATES 0.1115 [0.1111; 0.1119] 0.0 1.7 Western Asia
## UNITED KINGDOM 2.0046 [2.0040; 2.0052] 5.0 1.7 Northern Europe
## UNITED STATES 5.1806 [5.1802; 5.1811] 62.9 1.7 Northern America
## URUGUAY 0.2686 [0.2677; 0.2695] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.4848 [0.4844; 0.4852] 0.5 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 59
##
## rate 95%-CI z p-value
## Common effect model 2.7150 [2.7149; 2.7152] 30310.89 0
## Random effects model 0.2289 [0.1574; 0.3328] -7.72 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.1545 [1.6226; 5.2873]; tau = 1.4678 [1.2738; 2.2994]
## I^2 = 100.0%; H = 4506.10
##
## Test of heterogeneity:
## Q d.f. p-value
## 1177686897.84 58 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 4.9981 [4.9977; 4.9985] 14040867.96 100.0%
## Region = Central and South America and t ... 10 0.3631 [0.3630; 0.3633] 71651246.56 100.0%
## Region = Northern Europe 8 1.7810 [1.7805; 1.7814] 5007049.74 100.0%
## Region = Eastern Europe 8 0.2945 [0.2943; 0.2947] 14394809.46 100.0%
## Region = Southern Europe 6 1.2236 [1.2232; 1.2239] 14640133.22 100.0%
## Region = Western Europe 6 1.2828 [1.2825; 1.2831] 3594821.16 100.0%
## Region = Australia and New Zealand 2 0.8527 [0.8521; 0.8533] 232452.36 100.0%
## Region = Eastern Asia 4 0.2827 [0.2826; 0.2829] 35978409.65 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0266 [0.0265; 0.0266] 0.00 --
## Region = Southern Asia 2 0.0361 [0.0360; 0.0361] 234353.99 100.0%
## Region = Western Asia 6 1.2660 [1.2656; 1.2664] 11764429.76 100.0%
## Region = Northern Africa 3 0.0785 [0.0785; 0.0786] 453736.78 100.0%
## Region = Southern Africa 1 0.0456 [0.0455; 0.0457] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1005694587.18 12 0
## Within groups 171992310.65 46 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.6957 [1.9064; 7.1646] 0.2282 0.4777
## Region = Central and South America and t ... 10 0.1134 [0.0321; 0.4011] 4.1510 2.0374
## Region = Northern Europe 8 0.6376 [0.4455; 0.9124] 0.2675 0.5172
## Region = Eastern Europe 8 0.1305 [0.0528; 0.3223] 1.7037 1.3053
## Region = Southern Europe 6 0.6904 [0.3904; 1.2209] 0.5076 0.7125
## Region = Western Europe 6 0.8811 [0.7034; 1.1036] 0.0792 0.2814
## Region = Australia and New Zealand 2 0.9514 [0.6397; 1.4149] 0.0820 0.2864
## Region = Eastern Asia 4 0.0765 [0.0143; 0.4097] 2.9324 1.7124
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0266 [0.0265; 0.0266] -- --
## Region = Southern Asia 2 0.0400 [0.0296; 0.0540] 0.0469 0.2166
## Region = Western Asia 6 0.1550 [0.0408; 0.5888] 2.7809 1.6676
## Region = Northern Africa 3 0.0328 [0.0129; 0.0831] 0.6774 0.8231
## Region = Southern Africa 1 0.0456 [0.0455; 0.0457] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 128256.56 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1292 [0.1290; 0.1294] 0.2 1.7 Central and South America and the Caribbean
## AUSTRALIA 0.8366 [0.8360; 0.8373] 0.6 1.7 Australia and New Zealand
## AUSTRIA 1.7240 [1.7225; 1.7255] 0.5 1.7 Western Europe
## BELARUS 0.0122 [0.0121; 0.0124] 0.0 1.7 Eastern Europe
## BELGIUM 0.4468 [0.4461; 0.4475] 0.2 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0614 [0.0614; 0.0615] 0.4 1.7 Central and South America and the Caribbean
## BULGARIA 0.1616 [0.1611; 0.1621] 0.0 1.7 Eastern Europe
## CANADA 2.7823 [2.7814; 2.7832] 3.2 1.7 Northern America
## CHILE 0.0269 [0.0268; 0.0271] 0.0 1.7 Central and South America and the Caribbean
## CHINA 0.0031 [0.0031; 0.0031] 0.1 1.7 Eastern Asia
## COLOMBIA 0.0235 [0.0234; 0.0236] 0.0 1.7 Central and South America and the Caribbean
## CROATIA 0.2668 [0.2660; 0.2676] 0.0 1.7 Southern Europe
## CZECH REPUBLIC 1.0463 [1.0452; 1.0473] 0.4 1.7 Eastern Europe
## ECUADOR 0.0720 [0.0718; 0.0723] 0.0 1.7 Central and South America and the Caribbean
## EGYPT 0.1069 [0.1067; 0.1070] 0.3 1.7 Northern Africa
## ESTONIA 0.2425 [0.2411; 0.2439] 0.0 1.7 Northern Europe
## FINLAND 0.9824 [0.9810; 0.9837] 0.2 1.7 Northern Europe
## FRANCE 1.2534 [1.2529; 1.2538] 2.6 1.7 Western Europe
## GERMANY 1.4106 [1.4101; 1.4110] 3.8 1.7 Western Europe
## GREECE 0.8032 [0.8023; 0.8041] 0.3 1.7 Southern Europe
## HUNGARY 0.3559 [0.3553; 0.3565] 0.1 1.7 Eastern Europe
## INDIA 0.0373 [0.0373; 0.0373] 1.5 1.7 Southern Asia
## IRELAND 0.8461 [0.8447; 0.8475] 0.1 1.7 Northern Europe
## ITALY 0.5452 [0.5449; 0.5455] 1.1 1.7 Southern Europe
## JAPAN 0.1810 [0.1809; 0.1811] 0.8 1.7 Eastern Asia
## JORDAN 0.1086 [0.1082; 0.1090] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0240 [0.0237; 0.0243] 0.0 1.7 Western Asia
## LATVIA 0.4715 [0.4699; 0.4730] 0.0 1.7 Northern Europe
## LEBANON 0.3637 [0.3629; 0.3646] 0.1 1.7 Western Asia
## LITHUANIA 0.2311 [0.2302; 0.2320] 0.0 1.7 Northern Europe
## LUXEMBOURG 0.7427 [0.7388; 0.7466] 0.0 1.7 Western Europe
## MEXICO 0.0781 [0.0780; 0.0782] 0.3 1.7 Central and South America and the Caribbean
## MOROCCO 0.0060 [0.0060; 0.0061] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 1.3931 [1.3913; 1.3950] 0.2 1.7 Australia and New Zealand
## NORWAY 1.8065 [1.8046; 1.8085] 0.3 1.7 Northern Europe
## PAKISTAN 0.0446 [0.0445; 0.0446] 0.3 1.7 Southern Asia
## PERU 0.0172 [0.0171; 0.0173] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0290 [0.0289; 0.0291] 0.1 1.7 South-eastern Asia
## POLAND 0.1920 [0.1917; 0.1922] 0.2 1.7 Eastern Europe
## PORTUGAL 1.1608 [1.1597; 1.1618] 0.4 1.7 Southern Europe
## PUERTO RICO 5.5328 [5.5288; 5.5368] 0.7 1.7 Central and South America and the Caribbean
## ROMANIA 0.2463 [0.2459; 0.2466] 0.2 1.7 Eastern Europe
## RUSSIA 0.0159 [0.0159; 0.0160] 0.1 1.7 Eastern Europe
## SAUDI ARABIA 0.1232 [0.1230; 0.1234] 0.1 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.8791 [0.8778; 0.8805] 0.2 1.7 Eastern Europe
## SLOVENIA 0.4079 [0.4064; 0.4093] 0.0 1.7 Southern Europe
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.7 Southern Africa
## SOUTH KOREA 0.6761 [0.6757; 0.6765] 1.1 1.7 Eastern Asia
## SPAIN 1.8213 [1.8206; 1.8219] 2.9 1.7 Southern Europe
## SWEDEN 1.2623 [1.2611; 1.2635] 0.4 1.7 Northern Europe
## SWITZERLAND 0.5748 [0.5739; 0.5757] 0.1 1.7 Western Europe
## TAIWAN 0.1970 [0.1967; 0.1973] 0.2 1.7 Eastern Asia
## THAILAND 0.1721 [0.1720; 0.1723] 0.4 1.7 South-eastern Asia
## TUNISIA 0.0815 [0.0812; 0.0818] 0.0 1.7 Northern Africa
## TÜRKIYE 1.7746 [1.7740; 1.7751] 4.3 1.7 Western Asia
## UNITED ARAB EMIRATES 0.1305 [0.1301; 0.1309] 0.0 1.7 Western Asia
## UNITED KINGDOM 2.3586 [2.3580; 2.3592] 5.0 1.7 Northern Europe
## UNITED STATES 6.3255 [6.3250; 6.3260] 65.3 1.7 Northern America
## URUGUAY 0.2401 [0.2392; 0.2409] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.4784 [0.4780; 0.4788] 0.5 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.2965 [3.2963; 3.2967] 39447.96 0
## Random effects model 0.2505 [0.1693; 0.3707] -6.92 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.3975 [1.6822; 5.4854]; tau = 1.5484 [1.2970; 2.3421]
## I^2 = 100.0%; H = 5009.64
##
## Test of heterogeneity:
## Q d.f. p-value
## 1480692150.09 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 6.0892 [6.0888; 6.0896] 22322857.16 100.0%
## Region = Central and South America and t ... 10 0.4215 [0.4213; 0.4217] 83966945.48 100.0%
## Region = Northern Europe 8 2.0853 [2.0848; 2.0858] 6851246.20 100.0%
## Region = Eastern Europe 8 0.3698 [0.3696; 0.3700] 16162172.26 100.0%
## Region = Southern Europe 6 1.2310 [1.2306; 1.2313] 14580988.44 100.0%
## Region = Western Europe 6 1.3086 [1.3083; 1.3089] 3905460.33 100.0%
## Region = Australia and New Zealand 2 0.9491 [0.9484; 0.9497] 435233.77 100.0%
## Region = Eastern Asia 4 0.2746 [0.2744; 0.2747] 42693447.47 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.1226 [0.1225; 0.1227] 2555118.07 100.0%
## Region = Southern Asia 2 0.0383 [0.0383; 0.0383] 78155.30 100.0%
## Region = Western Asia 6 1.5620 [1.5615; 1.5624] 15136013.33 100.0%
## Region = Northern Africa 3 0.0986 [0.0985; 0.0987] 588757.35 100.0%
## Region = Southern Africa 1 0.0456 [0.0455; 0.0457] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1271415754.90 12 0
## Within groups 209276395.18 47 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 4.1952 [1.8759; 9.3820] 0.3373 0.5807
## Region = Central and South America and t ... 10 0.1119 [0.0298; 0.4204] 4.5582 2.1350
## Region = Northern Europe 8 0.7657 [0.5139; 1.1407] 0.3311 0.5754
## Region = Eastern Europe 8 0.1625 [0.0685; 0.3859] 1.5564 1.2476
## Region = Southern Europe 6 0.6821 [0.3875; 1.2008] 0.4995 0.7067
## Region = Western Europe 6 0.9136 [0.7246; 1.1519] 0.0839 0.2897
## Region = Australia and New Zealand 2 1.0796 [0.6550; 1.7795] 0.1300 0.3606
## Region = Eastern Asia 4 0.0929 [0.0172; 0.5003] 2.9520 1.7181
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.0706 [0.0123; 0.4047] 1.5861 1.2594
## Region = Southern Asia 2 0.0408 [0.0343; 0.0485] 0.0157 0.1253
## Region = Western Asia 6 0.1732 [0.0433; 0.6927] 3.0006 1.7322
## Region = Northern Africa 3 0.0375 [0.0133; 0.1054] 0.8352 0.9139
## Region = Southern Africa 1 0.0456 [0.0455; 0.0457] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1213.87 12 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1364 [0.1363; 0.1366] 0.2 1.6 Central and South America and the Caribbean
## AUSTRALIA 0.8668 [0.8662; 0.8675] 0.6 1.6 Australia and New Zealand
## AUSTRIA 1.8391 [1.8376; 1.8406] 0.5 1.6 Western Europe
## BELARUS 0.0139 [0.0138; 0.0140] 0.0 1.6 Eastern Europe
## BELGIUM 0.4884 [0.4878; 0.4891] 0.2 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.0443 [0.0439; 0.0447] 0.0 1.6 Southern Europe
## BRAZIL 0.0770 [0.0769; 0.0770] 0.5 1.6 Central and South America and the Caribbean
## BULGARIA 0.2211 [0.2206; 0.2217] 0.1 1.6 Eastern Europe
## CANADA 2.9905 [2.9896; 2.9915] 3.2 1.6 Northern America
## CHILE 0.0234 [0.0233; 0.0235] 0.0 1.6 Central and South America and the Caribbean
## CHINA 0.0047 [0.0047; 0.0047] 0.2 1.6 Eastern Asia
## COLOMBIA 0.0219 [0.0218; 0.0220] 0.0 1.6 Central and South America and the Caribbean
## CROATIA 0.2282 [0.2275; 0.2290] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.2108 [1.2097; 1.2119] 0.4 1.6 Eastern Europe
## ECUADOR 0.0672 [0.0670; 0.0675] 0.0 1.6 Central and South America and the Caribbean
## EGYPT 0.1148 [0.1147; 0.1149] 0.3 1.6 Northern Africa
## ESTONIA 0.2902 [0.2887; 0.2917] 0.0 1.6 Northern Europe
## FINLAND 1.0435 [1.0420; 1.0449] 0.2 1.6 Northern Europe
## FRANCE 1.2042 [1.2038; 1.2047] 2.4 1.6 Western Europe
## GERMANY 1.4962 [1.4957; 1.4966] 3.8 1.6 Western Europe
## GREECE 0.8916 [0.8906; 0.8925] 0.3 1.6 Southern Europe
## HUNGARY 0.4230 [0.4223; 0.4236] 0.1 1.6 Eastern Europe
## INDIA 0.0430 [0.0430; 0.0430] 1.7 1.6 Southern Asia
## IRELAND 0.8952 [0.8938; 0.8967] 0.1 1.6 Northern Europe
## ITALY 0.5448 [0.5445; 0.5451] 1.0 1.6 Southern Europe
## JAPAN 0.1505 [0.1504; 0.1506] 0.6 1.6 Eastern Asia
## JORDAN 0.1083 [0.1079; 0.1087] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0189 [0.0187; 0.0190] 0.0 1.6 Central Asia
## KUWAIT 0.0285 [0.0282; 0.0288] 0.0 1.6 Western Asia
## LATVIA 0.6091 [0.6073; 0.6108] 0.0 1.6 Northern Europe
## LEBANON 0.3338 [0.3330; 0.3346] 0.1 1.6 Western Asia
## LITHUANIA 0.2378 [0.2369; 0.2387] 0.0 1.6 Northern Europe
## LUXEMBOURG 0.7418 [0.7379; 0.7456] 0.0 1.6 Western Europe
## MEXICO 0.0770 [0.0769; 0.0771] 0.3 1.6 Central and South America and the Caribbean
## MOROCCO 0.0104 [0.0103; 0.0104] 0.0 1.6 Northern Africa
## NETHERLANDS 0.6528 [0.6522; 0.6534] 0.3 1.6 Western Europe
## NEW ZEALAND 1.6064 [1.6045; 1.6084] 0.2 1.6 Australia and New Zealand
## NORWAY 2.0397 [2.0376; 2.0418] 0.3 1.6 Northern Europe
## PAKISTAN 0.0465 [0.0465; 0.0466] 0.3 1.6 Southern Asia
## PERU 0.0192 [0.0192; 0.0193] 0.0 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0279 [0.0279; 0.0280] 0.1 1.6 South-eastern Asia
## POLAND 0.2211 [0.2208; 0.2213] 0.3 1.6 Eastern Europe
## PORTUGAL 1.1147 [1.1136; 1.1157] 0.4 1.6 Southern Europe
## PUERTO RICO 7.1769 [7.1723; 7.1815] 0.8 1.6 Central and South America and the Caribbean
## ROMANIA 0.3992 [0.3987; 0.3996] 0.3 1.6 Eastern Europe
## RUSSIA 0.0184 [0.0183; 0.0184] 0.1 1.6 Eastern Europe
## SAUDI ARABIA 0.1683 [0.1681; 0.1686] 0.1 1.6 Western Asia
## SERBIA 0.0660 [0.0657; 0.0663] 0.0 1.6 Southern Europe
## SLOVAKIA 0.9711 [0.9697; 0.9724] 0.2 1.6 Eastern Europe
## SLOVENIA 0.3702 [0.3688; 0.3715] 0.0 1.6 Southern Europe
## SOUTH AFRICA 0.0503 [0.0502; 0.0504] 0.1 1.6 Southern Africa
## SOUTH KOREA 0.6730 [0.6727; 0.6734] 1.0 1.6 Eastern Asia
## SPAIN 1.8636 [1.8630; 1.8643] 2.7 1.6 Southern Europe
## SWEDEN 1.4097 [1.4084; 1.4109] 0.4 1.6 Northern Europe
## SWITZERLAND 0.5369 [0.5360; 0.5377] 0.1 1.6 Western Europe
## TAIWAN 0.2174 [0.2171; 0.2177] 0.2 1.6 Eastern Asia
## THAILAND 0.2549 [0.2547; 0.2551] 0.5 1.6 South-eastern Asia
## TUNISIA 0.0891 [0.0888; 0.0894] 0.0 1.6 Northern Africa
## TÜRKIYE 2.1980 [2.1975; 2.1986] 5.0 1.6 Western Asia
## UNITED ARAB EMIRATES 0.1306 [0.1302; 0.1310] 0.0 1.6 Western Asia
## UNITED KINGDOM 2.7146 [2.7139; 2.7152] 5.4 1.6 Northern Europe
## UNITED STATES 6.5599 [6.5594; 6.5604] 63.7 1.6 Northern America
## URUGUAY 0.3039 [0.3029; 0.3049] 0.0 1.6 Central and South America and the Caribbean
## VENEZUELA 0.5095 [0.5091; 0.5100] 0.5 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.3835 [3.3833; 3.3837] 41728.55 0
## Random effects model 0.2547 [0.1748; 0.3712] -7.12 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.3641 [1.7027; 5.3863]; tau = 1.5376 [1.3049; 2.3208]
## I^2 = 100.0%; H = 5068.59
##
## Test of heterogeneity:
## Q d.f. p-value
## 1618509242.42 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 6.3167 [6.3162; 6.3171] 22159370.66 100.0%
## Region = Central and South America and t ... 10 0.5506 [0.5504; 0.5508] 109018020.17 100.0%
## Region = Northern Europe 8 2.3962 [2.3957; 2.3967] 8260655.14 100.0%
## Region = Eastern Europe 8 0.4438 [0.4436; 0.4440] 18059780.13 100.0%
## Region = Southern Europe 8 1.2378 [1.2375; 1.2382] 17724703.98 100.0%
## Region = Western Europe 7 1.2989 [1.2986; 1.2992] 6752609.30 100.0%
## Region = Australia and New Zealand 2 1.0217 [1.0211; 1.0224] 723732.35 100.0%
## Region = Eastern Asia 4 0.2378 [0.2378; 0.2379] 51274998.68 100.0%
## Region = Central Asia 1 0.0189 [0.0187; 0.0190] 0.00 --
## Region = South-eastern Asia 2 0.1894 [0.1893; 0.1896] 4126909.43 100.0%
## Region = Southern Asia 2 0.0435 [0.0435; 0.0435] 16705.05 100.0%
## Region = Western Asia 6 1.9348 [1.9343; 1.9352] 19487910.69 100.0%
## Region = Northern Africa 3 0.1043 [0.1042; 0.1044] 703123.53 100.0%
## Region = Southern Africa 1 0.0503 [0.0502; 0.0504] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1360200723.30 13 0
## Within groups 258308519.12 50 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 4.4292 [2.0512; 9.5641] 0.3085 0.5554
## Region = Central and South America and t ... 10 0.1195 [0.0292; 0.4900] 5.1801 2.2760
## Region = Northern Europe 8 0.8626 [0.5704; 1.3043] 0.3561 0.5967
## Region = Eastern Europe 8 0.1991 [0.0874; 0.4539] 1.4137 1.1890
## Region = Southern Europe 8 0.3545 [0.2079; 0.6045] 0.5933 0.7703
## Region = Western Europe 7 0.8837 [0.6779; 1.1519] 0.1280 0.3578
## Region = Australia and New Zealand 2 1.1800 [0.6447; 2.1600] 0.1903 0.4362
## Region = Eastern Asia 4 0.1011 [0.0162; 0.6318] 3.4972 1.8701
## Region = Central Asia 1 0.0189 [0.0187; 0.0190] -- --
## Region = South-eastern Asia 2 0.0844 [0.0097; 0.7368] 2.4447 1.5636
## Region = Southern Asia 2 0.0447 [0.0414; 0.0483] 0.0031 0.0557
## Region = Western Asia 6 0.1919 [0.0456; 0.8063] 3.2196 1.7943
## Region = Northern Africa 3 0.0474 [0.0170; 0.1319] 0.8200 0.9055
## Region = Southern Africa 1 0.0503 [0.0502; 0.0504] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 98506.15 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1383 [0.1382; 0.1385] 0.2 1.6 Central and South America and the Caribbean
## AUSTRALIA 0.9460 [0.9453; 0.9466] 0.7 1.6 Australia and New Zealand
## AUSTRIA 1.9626 [1.9611; 1.9642] 0.5 1.6 Western Europe
## BELARUS 0.0148 [0.0147; 0.0150] 0.0 1.6 Eastern Europe
## BELGIUM 0.5723 [0.5715; 0.5730] 0.2 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.0958 [0.0953; 0.0964] 0.0 1.6 Southern Europe
## BRAZIL 0.0876 [0.0875; 0.0876] 0.5 1.6 Central and South America and the Caribbean
## BULGARIA 0.2866 [0.2859; 0.2872] 0.1 1.6 Eastern Europe
## CANADA 3.3044 [3.3034; 3.3054] 3.5 1.6 Northern America
## CHILE 0.0220 [0.0219; 0.0221] 0.0 1.6 Central and South America and the Caribbean
## CHINA 0.0083 [0.0083; 0.0083] 0.4 1.6 Eastern Asia
## COLOMBIA 0.0193 [0.0192; 0.0193] 0.0 1.6 Central and South America and the Caribbean
## CROATIA 0.2042 [0.2035; 0.2049] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.3418 [1.3407; 1.3430] 0.4 1.6 Eastern Europe
## ECUADOR 0.0590 [0.0588; 0.0592] 0.0 1.6 Central and South America and the Caribbean
## EGYPT 0.1044 [0.1043; 0.1045] 0.3 1.6 Northern Africa
## ESTONIA 0.3901 [0.3883; 0.3919] 0.0 1.6 Northern Europe
## FINLAND 1.1761 [1.1746; 1.1776] 0.2 1.6 Northern Europe
## FRANCE 1.2173 [1.2168; 1.2177] 2.4 1.6 Western Europe
## GERMANY 1.5603 [1.5599; 1.5608] 3.9 1.6 Western Europe
## GREECE 0.8239 [0.8230; 0.8248] 0.3 1.6 Southern Europe
## HUNGARY 0.4641 [0.4634; 0.4648] 0.1 1.6 Eastern Europe
## INDIA 0.0472 [0.0472; 0.0473] 1.8 1.6 Southern Asia
## IRELAND 0.9214 [0.9199; 0.9228] 0.1 1.6 Northern Europe
## ITALY 0.5310 [0.5307; 0.5313] 1.0 1.6 Southern Europe
## JAPAN 0.1244 [0.1243; 0.1245] 0.5 1.6 Eastern Asia
## JORDAN 0.0998 [0.0994; 0.1002] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0218 [0.0217; 0.0220] 0.0 1.6 Central Asia
## KUWAIT 0.0292 [0.0289; 0.0295] 0.0 1.6 Western Asia
## LATVIA 0.8136 [0.8115; 0.8156] 0.1 1.6 Northern Europe
## LEBANON 0.2992 [0.2985; 0.3000] 0.1 1.6 Western Asia
## LITHUANIA 0.2617 [0.2607; 0.2626] 0.0 1.6 Northern Europe
## LUXEMBOURG 0.7475 [0.7437; 0.7514] 0.0 1.6 Western Europe
## MEXICO 0.0837 [0.0836; 0.0837] 0.3 1.6 Central and South America and the Caribbean
## MOROCCO 0.0172 [0.0171; 0.0173] 0.0 1.6 Northern Africa
## NETHERLANDS 0.6605 [0.6599; 0.6612] 0.3 1.6 Western Europe
## NEW ZEALAND 1.8861 [1.8840; 1.8882] 0.3 1.6 Australia and New Zealand
## NORWAY 2.1749 [2.1727; 2.1770] 0.3 1.6 Northern Europe
## PAKISTAN 0.0469 [0.0469; 0.0470] 0.3 1.6 Southern Asia
## PERU 0.0190 [0.0189; 0.0191] 0.0 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0268 [0.0267; 0.0268] 0.1 1.6 South-eastern Asia
## POLAND 0.2055 [0.2053; 0.2058] 0.2 1.6 Eastern Europe
## PORTUGAL 1.1942 [1.1931; 1.1953] 0.4 1.6 Southern Europe
## PUERTO RICO 8.2103 [8.2053; 8.2152] 0.9 1.6 Central and South America and the Caribbean
## ROMANIA 0.5151 [0.5146; 0.5157] 0.3 1.6 Eastern Europe
## RUSSIA 0.0226 [0.0225; 0.0226] 0.1 1.6 Eastern Europe
## SAUDI ARABIA 0.1279 [0.1277; 0.1281] 0.1 1.6 Western Asia
## SERBIA 0.0827 [0.0823; 0.0830] 0.0 1.6 Southern Europe
## SLOVAKIA 1.0650 [1.0635; 1.0664] 0.2 1.6 Eastern Europe
## SLOVENIA 0.3544 [0.3531; 0.3558] 0.0 1.6 Southern Europe
## SOUTH AFRICA 0.0532 [0.0531; 0.0533] 0.1 1.6 Southern Africa
## SOUTH KOREA 0.7229 [0.7225; 0.7232] 1.1 1.6 Eastern Asia
## SPAIN 1.8152 [1.8146; 1.8159] 2.6 1.6 Southern Europe
## SWEDEN 1.5277 [1.5265; 1.5290] 0.4 1.6 Northern Europe
## SWITZERLAND 0.5176 [0.5168; 0.5184] 0.1 1.6 Western Europe
## TAIWAN 0.2142 [0.2139; 0.2145] 0.2 1.6 Eastern Asia
## THAILAND 0.2950 [0.2948; 0.2952] 0.6 1.6 South-eastern Asia
## TUNISIA 0.1064 [0.1061; 0.1067] 0.0 1.6 Northern Africa
## TÜRKIYE 2.3781 [2.3775; 2.3787] 5.4 1.6 Western Asia
## UNITED ARAB EMIRATES 0.0810 [0.0807; 0.0813] 0.0 1.6 Western Asia
## UNITED KINGDOM 3.2081 [3.2074; 3.2089] 6.3 1.6 Northern Europe
## UNITED STATES 6.3591 [6.3586; 6.3595] 61.2 1.6 Northern America
## URUGUAY 0.4181 [0.4169; 0.4192] 0.0 1.6 Central and South America and the Caribbean
## VENEZUELA 0.5864 [0.5860; 0.5869] 0.5 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.2768 [3.2767; 3.2770] 40970.80 0
## Random effects model 0.2735 [0.1891; 0.3955] -6.89 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.2668 [1.7021; 5.3176]; tau = 1.5056 [1.3047; 2.3060]
## I^2 = 100.0%; H = 5128.07
##
## Test of heterogeneity:
## Q d.f. p-value
## 1656717916.72 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 6.1357 [6.1352; 6.1361] 17075245.37 100.0%
## Region = Central and South America and t ... 10 0.6394 [0.6391; 0.6396] 124192415.77 100.0%
## Region = Northern Europe 8 2.8154 [2.8148; 2.8160] 10578709.84 100.0%
## Region = Eastern Europe 8 0.4962 [0.4960; 0.4964] 20800619.61 100.0%
## Region = Southern Europe 8 1.2063 [1.2060; 1.2066] 17661915.48 100.0%
## Region = Western Europe 7 1.3442 [1.3439; 1.3444] 7340926.83 100.0%
## Region = Australia and New Zealand 2 1.1476 [1.1470; 1.1483] 1055334.37 100.0%
## Region = Eastern Asia 4 0.2080 [0.2079; 0.2080] 65605634.82 100.0%
## Region = Central Asia 1 0.0218 [0.0217; 0.0220] 0.00 --
## Region = South-eastern Asia 2 0.2238 [0.2237; 0.2240] 4843774.97 100.0%
## Region = Southern Asia 2 0.0472 [0.0472; 0.0472] 104.88 99.0%
## Region = Western Asia 6 2.1359 [2.1353; 2.1364] 20203034.60 100.0%
## Region = Northern Africa 3 0.0950 [0.0949; 0.0951] 644510.30 100.0%
## Region = Southern Africa 1 0.0532 [0.0531; 0.0533] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1366715689.87 13 0
## Within groups 290002226.85 50 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 4.5840 [2.4134; 8.7067] 0.2143 0.4629
## Region = Central and South America and t ... 10 0.1255 [0.0303; 0.5205] 5.2682 2.2953
## Region = Northern Europe 8 0.9948 [0.6378; 1.5517] 0.4115 0.6415
## Region = Eastern Europe 8 0.2257 [0.0979; 0.5205] 1.4535 1.2056
## Region = Southern Europe 8 0.3908 [0.2287; 0.6677] 0.5977 0.7731
## Region = Western Europe 7 0.9170 [0.6991; 1.2027] 0.1341 0.3661
## Region = Australia and New Zealand 2 1.3357 [0.6793; 2.6267] 0.2381 0.4879
## Region = Eastern Asia 4 0.1125 [0.0154; 0.8223] 4.1216 2.0302
## Region = Central Asia 1 0.0218 [0.0217; 0.0220] -- --
## Region = South-eastern Asia 2 0.0889 [0.0085; 0.9334] 2.8792 1.6968
## Region = Southern Asia 2 0.0471 [0.0468; 0.0474] <0.0001 0.0043
## Region = Western Asia 6 0.1667 [0.0334; 0.8321] 4.0376 2.0094
## Region = Northern Africa 3 0.0576 [0.0242; 0.1368] 0.5843 0.7644
## Region = Southern Africa 1 0.0532 [0.0531; 0.0533] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 94816.14 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1348 [0.1346; 0.1350] 0.2 1.6 Central and South America and the Caribbean
## AUSTRALIA 0.7847 [0.7841; 0.7853] 0.5 1.6 Australia and New Zealand
## AUSTRIA 2.0660 [2.0644; 2.0676] 0.5 1.6 Western Europe
## BELARUS 0.0221 [0.0219; 0.0222] 0.0 1.6 Eastern Europe
## BELGIUM 0.8101 [0.8093; 0.8110] 0.2 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.1360 [0.1353; 0.1366] 0.0 1.6 Southern Europe
## BRAZIL 0.0978 [0.0977; 0.0978] 0.5 1.6 Central and South America and the Caribbean
## BULGARIA 0.4003 [0.3995; 0.4010] 0.1 1.6 Eastern Europe
## CANADA 3.6682 [3.6672; 3.6693] 3.5 1.6 Northern America
## CHILE 0.0206 [0.0205; 0.0207] 0.0 1.6 Central and South America and the Caribbean
## CHINA 0.0119 [0.0119; 0.0119] 0.4 1.6 Eastern Asia
## COLOMBIA 0.0168 [0.0167; 0.0169] 0.0 1.6 Central and South America and the Caribbean
## CROATIA 0.1891 [0.1884; 0.1897] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.5003 [1.4991; 1.5015] 0.4 1.6 Eastern Europe
## ECUADOR 0.0490 [0.0488; 0.0492] 0.0 1.6 Central and South America and the Caribbean
## EGYPT 0.1103 [0.1102; 0.1104] 0.3 1.6 Northern Africa
## ESTONIA 0.5081 [0.5061; 0.5101] 0.0 1.6 Northern Europe
## FINLAND 1.4016 [1.4000; 1.4032] 0.2 1.6 Northern Europe
## FRANCE 1.2245 [1.2240; 1.2249] 2.1 1.6 Western Europe
## GERMANY 1.6432 [1.6427; 1.6436] 3.6 1.6 Western Europe
## GREECE 0.9298 [0.9289; 0.9308] 0.3 1.6 Southern Europe
## HUNGARY 0.5382 [0.5374; 0.5389] 0.1 1.6 Eastern Europe
## INDIA 0.0518 [0.0517; 0.0518] 1.8 1.6 Southern Asia
## IRELAND 0.9678 [0.9663; 0.9693] 0.1 1.6 Northern Europe
## ITALY 0.5287 [0.5284; 0.5290] 0.9 1.6 Southern Europe
## JAPAN 0.1114 [0.1113; 0.1115] 0.4 1.6 Eastern Asia
## JORDAN 0.1073 [0.1069; 0.1076] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0203 [0.0202; 0.0204] 0.0 1.6 Central Asia
## KUWAIT 0.0408 [0.0404; 0.0411] 0.0 1.6 Western Asia
## LATVIA 1.0487 [1.0464; 1.0510] 0.1 1.6 Northern Europe
## LEBANON 0.3187 [0.3180; 0.3195] 0.1 1.6 Western Asia
## LITHUANIA 0.3129 [0.3118; 0.3139] 0.0 1.6 Northern Europe
## LUXEMBOURG 0.7067 [0.7030; 0.7104] 0.0 1.6 Western Europe
## MEXICO 0.0904 [0.0903; 0.0905] 0.3 1.6 Central and South America and the Caribbean
## MOROCCO 0.0270 [0.0269; 0.0271] 0.0 1.6 Northern Africa
## NETHERLANDS 0.6591 [0.6585; 0.6598] 0.3 1.6 Western Europe
## NEW ZEALAND 2.1377 [2.1355; 2.1399] 0.3 1.6 Australia and New Zealand
## NORWAY 2.2712 [2.2690; 2.2734] 0.3 1.6 Northern Europe
## PAKISTAN 0.0450 [0.0449; 0.0450] 0.2 1.6 Southern Asia
## PERU 0.0181 [0.0180; 0.0182] 0.0 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0330 [0.0330; 0.0331] 0.1 1.6 South-eastern Asia
## POLAND 0.2660 [0.2658; 0.2663] 0.3 1.6 Eastern Europe
## PORTUGAL 1.2241 [1.2230; 1.2252] 0.3 1.6 Southern Europe
## PUERTO RICO 8.3869 [8.3819; 8.3919] 0.8 1.6 Central and South America and the Caribbean
## ROMANIA 0.6903 [0.6897; 0.6909] 0.4 1.6 Eastern Europe
## RUSSIA 0.0296 [0.0295; 0.0296] 0.1 1.6 Eastern Europe
## SAUDI ARABIA 0.1192 [0.1190; 0.1194] 0.1 1.6 Western Asia
## SERBIA 0.0894 [0.0891; 0.0897] 0.0 1.6 Southern Europe
## SLOVAKIA 1.1862 [1.1847; 1.1877] 0.2 1.6 Eastern Europe
## SLOVENIA 0.3501 [0.3488; 0.3515] 0.0 1.6 Southern Europe
## SOUTH AFRICA 0.0572 [0.0571; 0.0573] 0.1 1.6 Southern Africa
## SOUTH KOREA 0.7358 [0.7354; 0.7362] 1.0 1.6 Eastern Asia
## SPAIN 1.8345 [1.8339; 1.8352] 2.3 1.6 Southern Europe
## SWEDEN 1.8971 [1.8957; 1.8986] 0.5 1.6 Northern Europe
## SWITZERLAND 0.5102 [0.5094; 0.5110] 0.1 1.6 Western Europe
## TAIWAN 0.2000 [0.1997; 0.2003] 0.1 1.6 Eastern Asia
## THAILAND 0.4366 [0.4363; 0.4368] 0.8 1.6 South-eastern Asia
## TUNISIA 0.1107 [0.1104; 0.1111] 0.0 1.6 Northern Africa
## TÜRKIYE 2.5445 [2.5439; 2.5451] 5.2 1.6 Western Asia
## UNITED ARAB EMIRATES 0.0828 [0.0825; 0.0831] 0.0 1.6 Western Asia
## UNITED KINGDOM 3.8409 [3.8401; 3.8417] 6.7 1.6 Northern Europe
## UNITED STATES 7.3525 [7.3520; 7.3530] 62.5 1.6 Northern America
## URUGUAY 0.4117 [0.4106; 0.4128] 0.0 1.6 Central and South America and the Caribbean
## VENEZUELA 0.6841 [0.6836; 0.6846] 0.5 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.7911 [3.7909; 3.7913] 49132.25 0
## Random effects model 0.3009 [0.2060; 0.4396] -6.21 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.3923 [1.7292; 5.4420]; tau = 1.5467 [1.3150; 2.3328]
## I^2 = 100.0%; H = 5554.13
##
## Test of heterogeneity:
## Q d.f. p-value
## 1943447617.03 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 7.0878 [7.0873; 7.0883] 21658699.79 100.0%
## Region = Central and South America and t ... 10 0.6512 [0.6510; 0.6514] 126291949.71 100.0%
## Region = Northern Europe 8 3.3625 [3.3618; 3.3631] 12984168.11 100.0%
## Region = Eastern Europe 8 0.5736 [0.5734; 0.5739] 23620137.19 100.0%
## Region = Southern Europe 8 1.2226 [1.2222; 1.2229] 17899253.57 100.0%
## Region = Western Europe 7 1.4001 [1.3998; 1.4004] 7634003.02 100.0%
## Region = Australia and New Zealand 2 1.1101 [1.1094; 1.1108] 2316862.49 100.0%
## Region = Eastern Asia 4 0.1818 [0.1817; 0.1819] 72711690.90 100.0%
## Region = Central Asia 1 0.0203 [0.0202; 0.0204] 0.00 --
## Region = South-eastern Asia 2 0.3382 [0.3380; 0.3384] 7162697.36 100.0%
## Region = Southern Asia 2 0.0509 [0.0509; 0.0510] 55258.47 100.0%
## Region = Western Asia 6 2.2917 [2.2912; 2.2922] 21934345.55 100.0%
## Region = Northern Africa 3 0.0991 [0.0990; 0.0992] 608514.47 100.0%
## Region = Southern Africa 1 0.0572 [0.0571; 0.0573] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1628570036.40 13 0
## Within groups 314877580.64 50 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 5.1933 [2.6273; 10.2654] 0.2417 0.4917
## Region = Central and South America and t ... 10 0.1241 [0.0311; 0.4946] 4.9794 2.2314
## Region = Northern Europe 8 1.1794 [0.7480; 1.8594] 0.4317 0.6570
## Region = Eastern Europe 8 0.2869 [0.1285; 0.6406] 1.3436 1.1592
## Region = Southern Europe 8 0.4155 [0.2439; 0.7077] 0.5906 0.7685
## Region = Western Europe 7 0.9687 [0.7397; 1.2686] 0.1325 0.3641
## Region = Australia and New Zealand 2 1.2952 [0.4851; 3.4582] 0.5022 0.7086
## Region = Eastern Asia 4 0.1183 [0.0157; 0.8921] 4.2519 2.0620
## Region = Central Asia 1 0.0203 [0.0202; 0.0204] -- --
## Region = South-eastern Asia 2 0.1201 [0.0096; 1.5078] 3.3337 1.8258
## Region = Southern Asia 2 0.0482 [0.0420; 0.0554] 0.0099 0.0997
## Region = Western Asia 6 0.1809 [0.0349; 0.9365] 4.2236 2.0551
## Region = Northern Africa 3 0.0691 [0.0322; 0.1480] 0.4536 0.6735
## Region = Southern Africa 1 0.0572 [0.0571; 0.0573] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 123056.22 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0031 [ 0.0031; 0.0032] 0.0 1.5 Northern Africa
## ARGENTINA 0.1286 [ 0.1284; 0.1288] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.6459 [ 0.6453; 0.6464] 0.4 1.5 Australia and New Zealand
## AUSTRIA 2.1770 [ 2.1754; 2.1786] 0.5 1.5 Western Europe
## BELARUS 0.0305 [ 0.0303; 0.0307] 0.0 1.5 Eastern Europe
## BELGIUM 1.0163 [ 1.0154; 1.0173] 0.3 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1421 [ 0.1414; 0.1427] 0.0 1.5 Southern Europe
## BRAZIL 0.1063 [ 0.1062; 0.1064] 0.5 1.5 Central and South America and the Caribbean
## BULGARIA 0.4875 [ 0.4867; 0.4884] 0.1 1.5 Eastern Europe
## CANADA 3.8718 [ 3.8707; 3.8729] 3.4 1.5 Northern America
## CHILE 0.0202 [ 0.0201; 0.0203] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0158 [ 0.0158; 0.0158] 0.5 1.5 Eastern Asia
## COLOMBIA 0.0150 [ 0.0150; 0.0151] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1837 [ 0.1830; 0.1844] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.7140 [ 1.7127; 1.7153] 0.4 1.5 Eastern Europe
## ECUADOR 0.0434 [ 0.0432; 0.0436] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1243 [ 0.1242; 0.1244] 0.3 1.5 Northern Africa
## ESTONIA 0.6371 [ 0.6349; 0.6394] 0.0 1.5 Northern Europe
## FINLAND 1.6351 [ 1.6333; 1.6368] 0.2 1.5 Northern Europe
## FRANCE 1.2639 [ 1.2634; 1.2644] 2.0 1.5 Western Europe
## GERMANY 1.6853 [ 1.6848; 1.6858] 3.4 1.5 Western Europe
## GREECE 0.9639 [ 0.9629; 0.9649] 0.3 1.5 Southern Europe
## HUNGARY 0.6103 [ 0.6095; 0.6111] 0.1 1.5 Eastern Europe
## INDIA 0.0637 [ 0.0637; 0.0637] 2.0 1.5 Southern Asia
## IRELAND 1.0037 [ 1.0022; 1.0052] 0.1 1.5 Northern Europe
## ITALY 0.5519 [ 0.5516; 0.5522] 0.8 1.5 Southern Europe
## JAPAN 0.1038 [ 0.1037; 0.1039] 0.3 1.5 Eastern Asia
## JORDAN 0.0884 [ 0.0880; 0.0887] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0272 [ 0.0270; 0.0273] 0.0 1.5 Central Asia
## KUWAIT 0.1028 [ 0.1022; 0.1033] 0.0 1.5 Western Asia
## LATVIA 1.2957 [ 1.2931; 1.2983] 0.1 1.5 Northern Europe
## LEBANON 0.3372 [ 0.3365; 0.3380] 0.1 1.5 Western Asia
## LITHUANIA 0.3355 [ 0.3344; 0.3366] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6660 [ 0.6625; 0.6696] 0.0 1.5 Western Europe
## MEXICO 0.0891 [ 0.0890; 0.0892] 0.3 1.5 Central and South America and the Caribbean
## MOROCCO 0.0311 [ 0.0310; 0.0312] 0.0 1.5 Northern Africa
## NETHERLANDS 0.6984 [ 0.6977; 0.6991] 0.3 1.5 Western Europe
## NEW ZEALAND 2.4379 [ 2.4355; 2.4403] 0.3 1.5 Australia and New Zealand
## NORWAY 2.2095 [ 2.2074; 2.2116] 0.3 1.5 Northern Europe
## PAKISTAN 0.0423 [ 0.0422; 0.0423] 0.2 1.5 Southern Asia
## PERU 0.0158 [ 0.0157; 0.0159] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0272 [ 0.0271; 0.0272] 0.1 1.5 South-eastern Asia
## POLAND 0.3332 [ 0.3329; 0.3335] 0.3 1.5 Eastern Europe
## PORTUGAL 1.2655 [ 1.2643; 1.2666] 0.3 1.5 Southern Europe
## PUERTO RICO 10.1429 [10.1373; 10.1484] 0.9 1.5 Central and South America and the Caribbean
## ROMANIA 0.8419 [ 0.8412; 0.8426] 0.4 1.5 Eastern Europe
## RUSSIA 0.0362 [ 0.0361; 0.0362] 0.1 1.5 Eastern Europe
## SAUDI ARABIA 0.1250 [ 0.1248; 0.1252] 0.1 1.5 Western Asia
## SERBIA 0.0908 [ 0.0905; 0.0912] 0.0 1.5 Southern Europe
## SLOVAKIA 1.4205 [ 1.4189; 1.4222] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3401 [ 0.3388; 0.3414] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0581 [ 0.0580; 0.0582] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.8166 [ 0.8162; 0.8170] 1.0 1.5 Eastern Asia
## SPAIN 1.8863 [ 1.8856; 1.8869] 2.2 1.5 Southern Europe
## SWEDEN 2.3524 [ 2.3508; 2.3540] 0.6 1.5 Northern Europe
## SWITZERLAND 0.5117 [ 0.5109; 0.5125] 0.1 1.5 Western Europe
## TAIWAN 0.1917 [ 0.1914; 0.1920] 0.1 1.5 Eastern Asia
## THAILAND 0.4858 [ 0.4856; 0.4861] 0.8 1.5 South-eastern Asia
## TUNISIA 0.1231 [ 0.1227; 0.1234] 0.0 1.5 Northern Africa
## TÜRKIYE 2.5723 [ 2.5717; 2.5729] 4.9 1.5 Western Asia
## UNITED ARAB EMIRATES 0.0897 [ 0.0893; 0.0900] 0.0 1.5 Western Asia
## UNITED KINGDOM 4.4910 [ 4.4902; 4.4919] 7.3 1.5 Northern Europe
## UNITED STATES 7.9211 [ 7.9206; 7.9216] 62.3 1.5 Northern America
## URUGUAY 0.4417 [ 0.4405; 0.4429] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.8366 [ 0.8360; 0.8371] 0.6 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.0622 [4.0620; 4.0624] 53922.93 0
## Random effects model 0.3039 [0.2077; 0.4447] -6.13 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.4511 [1.7673; 5.5514]; tau = 1.5656 [1.3294; 2.3561]
## I^2 = 100.0%; H = 5831.39
##
## Test of heterogeneity:
## Q d.f. p-value
## 2176329690.77 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 7.6324 [7.6319; 7.6329] 24502017.82 100.0%
## Region = Central and South America and t ... 10 0.8332 [0.8329; 0.8335] 150990965.00 100.0%
## Region = Northern Europe 8 3.9290 [3.9283; 3.9297] 15545676.98 100.0%
## Region = Eastern Europe 8 0.6699 [0.6696; 0.6701] 27464690.33 100.0%
## Region = Southern Europe 8 1.2578 [1.2575; 1.2582] 18217237.30 100.0%
## Region = Western Europe 7 1.4488 [1.4485; 1.4491] 7395333.57 100.0%
## Region = Australia and New Zealand 2 1.1316 [1.1309; 1.1323] 4146297.98 100.0%
## Region = Eastern Asia 4 0.1838 [0.1837; 0.1838] 83779536.81 100.0%
## Region = Central Asia 1 0.0272 [0.0270; 0.0273] 0.00 --
## Region = South-eastern Asia 2 0.3903 [0.3901; 0.3905] 7666743.77 100.0%
## Region = Southern Asia 2 0.0614 [0.0614; 0.0614] 461162.92 100.0%
## Region = Western Asia 6 2.3055 [2.3050; 2.3060] 23294204.76 100.0%
## Region = Northern Africa 4 0.1080 [0.1079; 0.1081] 1250157.76 100.0%
## Region = Southern Africa 1 0.0581 [0.0580; 0.0582] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1811615665.79 13 0
## Within groups 364714024.98 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 5.5380 [2.7460; 11.1686] 0.2562 0.5062
## Region = Central and South America and t ... 10 0.1253 [0.0299; 0.5250] 5.3451 2.3119
## Region = Northern Europe 8 1.3434 [0.8434; 2.1398] 0.4513 0.6718
## Region = Eastern Europe 8 0.3499 [0.1584; 0.7725] 1.3067 1.1431
## Region = Southern Europe 8 0.4229 [0.2493; 0.7174] 0.5814 0.7625
## Region = Western Europe 7 1.0166 [0.7852; 1.3162] 0.1216 0.3487
## Region = Australia and New Zealand 2 1.2548 [0.3414; 4.6121] 0.8822 0.9392
## Region = Eastern Asia 4 0.1265 [0.0161; 0.9959] 4.4318 2.1052
## Region = Central Asia 1 0.0272 [0.0270; 0.0273] -- --
## Region = South-eastern Asia 2 0.1149 [0.0068; 1.9386] 4.1561 2.0387
## Region = Southern Asia 2 0.0519 [0.0347; 0.0776] 0.0841 0.2900
## Region = Western Asia 6 0.2110 [0.0417; 1.0679] 4.1071 2.0266
## Region = Northern Africa 4 0.0350 [0.0147; 0.0829] 0.7766 0.8813
## Region = Southern Africa 1 0.0581 [0.0580; 0.0582] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 87486.00 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0272 [ 0.0271; 0.0273] 0.0 1.5 Northern Africa
## ARGENTINA 0.1302 [ 0.1300; 0.1303] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.6512 [ 0.6507; 0.6518] 0.3 1.5 Australia and New Zealand
## AUSTRIA 2.1760 [ 2.1744; 2.1777] 0.4 1.5 Western Europe
## BELARUS 0.0476 [ 0.0474; 0.0478] 0.0 1.5 Eastern Europe
## BELGIUM 1.0938 [ 1.0927; 1.0948] 0.3 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1982 [ 0.1974; 0.1990] 0.0 1.5 Southern Europe
## BRAZIL 0.1118 [ 0.1118; 0.1119] 0.5 1.5 Central and South America and the Caribbean
## BULGARIA 0.5419 [ 0.5410; 0.5428] 0.1 1.5 Eastern Europe
## CANADA 4.1539 [ 4.1528; 4.1550] 3.3 1.5 Northern America
## CHILE 0.0193 [ 0.0192; 0.0194] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0202 [ 0.0202; 0.0202] 0.6 1.5 Eastern Asia
## COLOMBIA 0.0150 [ 0.0149; 0.0150] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1533 [ 0.1527; 0.1539] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.8848 [ 1.8834; 1.8861] 0.4 1.5 Eastern Europe
## ECUADOR 0.0448 [ 0.0447; 0.0450] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1340 [ 0.1339; 0.1342] 0.3 1.5 Northern Africa
## ESTONIA 0.7565 [ 0.7540; 0.7589] 0.0 1.5 Northern Europe
## FINLAND 1.8835 [ 1.8816; 1.8854] 0.2 1.5 Northern Europe
## FRANCE 1.2950 [ 1.2946; 1.2955] 1.8 1.5 Western Europe
## GERMANY 1.6790 [ 1.6785; 1.6794] 3.0 1.5 Western Europe
## GREECE 1.0152 [ 1.0142; 1.0162] 0.2 1.5 Southern Europe
## HUNGARY 0.6659 [ 0.6651; 0.6668] 0.1 1.5 Eastern Europe
## INDIA 0.0748 [ 0.0748; 0.0749] 2.1 1.5 Southern Asia
## IRELAND 1.0668 [ 1.0652; 1.0684] 0.1 1.5 Northern Europe
## ITALY 0.5614 [ 0.5610; 0.5617] 0.7 1.5 Southern Europe
## JAPAN 0.0968 [ 0.0967; 0.0969] 0.3 1.5 Eastern Asia
## JORDAN 0.0728 [ 0.0725; 0.0731] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0261 [ 0.0259; 0.0262] 0.0 1.5 Central Asia
## KUWAIT 0.1504 [ 0.1497; 0.1510] 0.0 1.5 Western Asia
## LATVIA 1.5928 [ 1.5899; 1.5957] 0.1 1.5 Northern Europe
## LEBANON 0.3553 [ 0.3545; 0.3560] 0.1 1.5 Western Asia
## LITHUANIA 0.3776 [ 0.3765; 0.3788] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6946 [ 0.6910; 0.6982] 0.0 1.5 Western Europe
## MEXICO 0.0935 [ 0.0934; 0.0936] 0.2 1.5 Central and South America and the Caribbean
## MOROCCO 0.0421 [ 0.0420; 0.0423] 0.0 1.5 Northern Africa
## NETHERLANDS 0.7221 [ 0.7215; 0.7228] 0.3 1.5 Western Europe
## NEW ZEALAND 2.8493 [ 2.8467; 2.8518] 0.3 1.5 Australia and New Zealand
## NORWAY 2.6628 [ 2.6605; 2.6651] 0.3 1.5 Northern Europe
## PAKISTAN 0.0398 [ 0.0397; 0.0398] 0.2 1.5 Southern Asia
## PERU 0.0558 [ 0.0557; 0.0560] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0275 [ 0.0274; 0.0275] 0.1 1.5 South-eastern Asia
## POLAND 0.4023 [ 0.4020; 0.4027] 0.3 1.5 Eastern Europe
## PORTUGAL 1.3129 [ 1.3117; 1.3140] 0.3 1.5 Southern Europe
## PUERTO RICO 11.7963 [11.7902; 11.8024] 0.9 1.5 Central and South America and the Caribbean
## ROMANIA 0.9553 [ 0.9545; 0.9560] 0.4 1.5 Eastern Europe
## RUSSIA 0.0453 [ 0.0453; 0.0454] 0.1 1.5 Eastern Europe
## SAUDI ARABIA 0.2044 [ 0.2042; 0.2047] 0.1 1.5 Western Asia
## SERBIA 0.1135 [ 0.1131; 0.1138] 0.0 1.5 Southern Europe
## SLOVAKIA 1.5663 [ 1.5645; 1.5680] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3335 [ 0.3322; 0.3348] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0551 [ 0.0550; 0.0552] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.8380 [ 0.8375; 0.8384] 0.9 1.5 Eastern Asia
## SPAIN 1.9664 [ 1.9657; 1.9670] 2.0 1.5 Southern Europe
## SWEDEN 2.7401 [ 2.7384; 2.7418] 0.6 1.5 Northern Europe
## SWITZERLAND 0.5081 [ 0.5073; 0.5089] 0.1 1.5 Western Europe
## TAIWAN 0.2013 [ 0.2010; 0.2016] 0.1 1.5 Eastern Asia
## THAILAND 0.5087 [ 0.5084; 0.5090] 0.8 1.5 South-eastern Asia
## TUNISIA 0.0958 [ 0.0955; 0.0961] 0.0 1.5 Northern Africa
## TÜRKIYE 2.6150 [ 2.6144; 2.6156] 4.5 1.5 Western Asia
## UNITED ARAB EMIRATES 0.1007 [ 0.1004; 0.1011] 0.0 1.5 Western Asia
## UNITED KINGDOM 5.1176 [ 5.1167; 5.1185] 7.3 1.5 Northern Europe
## UNITED STATES 9.2026 [ 9.2020; 9.2031] 64.3 1.5 Northern America
## URUGUAY 0.3638 [ 0.3627; 0.3649] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.3521 [ 0.3517; 0.3524] 0.2 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.7307 [4.7305; 4.7309] 63664.08 0
## Random effects model 0.3411 [0.2293; 0.5074] -5.31 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.6697 [1.8264; 5.7894]; tau = 1.6339 [1.3515; 2.4061]
## I^2 = 100.0%; H = 6349.46
##
## Test of heterogeneity:
## Q d.f. p-value
## 2580199141.57 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 8.8562 [8.8557; 8.8567] 32916135.44 100.0%
## Region = Central and South America and t ... 10 0.8637 [0.8634; 0.8640] 178926198.99 100.0%
## Region = Northern Europe 8 4.4871 [4.4863; 4.4878] 17341214.90 100.0%
## Region = Eastern Europe 8 0.7319 [0.7316; 0.7321] 30772569.15 100.0%
## Region = Southern Europe 8 1.3070 [1.3066; 1.3073] 19356305.49 100.0%
## Region = Western Europe 7 1.4589 [1.4586; 1.4592] 6901479.64 100.0%
## Region = Australia and New Zealand 2 1.2795 [1.2787; 1.2803] 5674568.71 100.0%
## Region = Eastern Asia 4 0.1724 [0.1724; 0.1725] 88154541.10 100.0%
## Region = Central Asia 1 0.0261 [0.0259; 0.0262] 0.00 --
## Region = South-eastern Asia 2 0.4096 [0.4094; 0.4098] 8079936.31 100.0%
## Region = Southern Asia 2 0.0714 [0.0714; 0.0714] 1070003.54 100.0%
## Region = Western Asia 6 2.2947 [2.2942; 2.2952] 25907039.84 100.0%
## Region = Northern Africa 4 0.1059 [0.1058; 0.1060] 1437448.83 100.0%
## Region = Southern Africa 1 0.0551 [0.0550; 0.0552] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2163661699.62 13 0
## Within groups 416537441.95 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 6.1828 [2.8356; 13.4810] 0.3164 0.5625
## Region = Central and South America and t ... 10 0.1310 [0.0254; 0.6757] 7.0074 2.6472
## Region = Northern Europe 8 1.5547 [0.9868; 2.4496] 0.4304 0.6560
## Region = Eastern Europe 8 0.4152 [0.1894; 0.9100] 1.2824 1.1324
## Region = Southern Europe 8 0.4503 [0.2636; 0.7693] 0.5972 0.7728
## Region = Western Europe 7 1.0403 [0.8128; 1.3315] 0.1110 0.3331
## Region = Australia and New Zealand 2 1.3622 [0.3207; 5.7863] 1.0892 1.0437
## Region = Eastern Asia 4 0.1347 [0.0177; 1.0234] 4.2811 2.0691
## Region = Central Asia 1 0.0261 [0.0259; 0.0262] -- --
## Region = South-eastern Asia 2 0.1182 [0.0068; 2.0651] 4.2601 2.0640
## Region = Southern Asia 2 0.0546 [0.0294; 0.1013] 0.1996 0.4467
## Region = Western Asia 6 0.2437 [0.0564; 1.0533] 3.3466 1.8294
## Region = Northern Africa 4 0.0619 [0.0282; 0.1358] 0.6420 0.8012
## Region = Southern Africa 1 0.0551 [0.0550; 0.0552] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 82622.74 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0677 [ 0.0676; 0.0678] 0.1 1.5 Northern Africa
## ARGENTINA 0.1252 [ 0.1251; 0.1254] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.6366 [ 0.6361; 0.6371] 0.3 1.5 Australia and New Zealand
## AUSTRIA 2.1089 [ 2.1074; 2.1105] 0.4 1.5 Western Europe
## BELARUS 0.0535 [ 0.0532; 0.0537] 0.0 1.5 Eastern Europe
## BELGIUM 0.9712 [ 0.9703; 0.9722] 0.2 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.2626 [ 0.2617; 0.2635] 0.0 1.5 Southern Europe
## BRAZIL 0.1122 [ 0.1121; 0.1123] 0.5 1.5 Central and South America and the Caribbean
## BULGARIA 0.5557 [ 0.5548; 0.5566] 0.1 1.5 Eastern Europe
## CANADA 4.5476 [ 4.5464; 4.5487] 3.3 1.5 Northern America
## CHILE 0.0195 [ 0.0194; 0.0196] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0238 [ 0.0238; 0.0238] 0.7 1.5 Eastern Asia
## COLOMBIA 0.0147 [ 0.0147; 0.0148] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1439 [ 0.1433; 0.1445] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.9731 [ 1.9717; 1.9745] 0.4 1.5 Eastern Europe
## ECUADOR 0.0484 [ 0.0483; 0.0486] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1568 [ 0.1567; 0.1570] 0.3 1.5 Northern Africa
## ESTONIA 0.8935 [ 0.8908; 0.8961] 0.0 1.5 Northern Europe
## FINLAND 2.2200 [ 2.2180; 2.2221] 0.2 1.5 Northern Europe
## FRANCE 1.3220 [ 1.3215; 1.3224] 1.7 1.5 Western Europe
## GERMANY 1.6877 [ 1.6872; 1.6881] 2.7 1.5 Western Europe
## GREECE 1.0272 [ 1.0262; 1.0283] 0.2 1.5 Southern Europe
## HUNGARY 0.7149 [ 0.7140; 0.7157] 0.1 1.5 Eastern Europe
## INDIA 0.0872 [ 0.0871; 0.0872] 2.3 1.5 Southern Asia
## IRELAND 1.1315 [ 1.1300; 1.1331] 0.1 1.5 Northern Europe
## ITALY 0.5573 [ 0.5569; 0.5576] 0.7 1.5 Southern Europe
## JAPAN 0.0910 [ 0.0909; 0.0910] 0.2 1.5 Eastern Asia
## JORDAN 0.0856 [ 0.0852; 0.0859] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0254 [ 0.0253; 0.0255] 0.0 1.5 Central Asia
## KUWAIT 0.4524 [ 0.4513; 0.4535] 0.0 1.5 Western Asia
## LATVIA 1.7258 [ 1.7228; 1.7288] 0.1 1.5 Northern Europe
## LEBANON 0.4366 [ 0.4357; 0.4374] 0.1 1.5 Western Asia
## LITHUANIA 0.4294 [ 0.4282; 0.4307] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6676 [ 0.6641; 0.6710] 0.0 1.5 Western Europe
## MEXICO 0.1015 [ 0.1014; 0.1016] 0.2 1.5 Central and South America and the Caribbean
## MOROCCO 0.0514 [ 0.0512; 0.0515] 0.0 1.5 Northern Africa
## NETHERLANDS 0.7640 [ 0.7633; 0.7647] 0.3 1.5 Western Europe
## NEW ZEALAND 3.3486 [ 3.3459; 3.3514] 0.3 1.5 Australia and New Zealand
## NORWAY 2.7479 [ 2.7456; 2.7503] 0.3 1.5 Northern Europe
## PAKISTAN 0.0364 [ 0.0364; 0.0365] 0.1 1.5 Southern Asia
## PERU 0.0566 [ 0.0565; 0.0568] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0275 [ 0.0274; 0.0275] 0.1 1.5 South-eastern Asia
## POLAND 0.4448 [ 0.4445; 0.4452] 0.3 1.5 Eastern Europe
## PORTUGAL 1.3466 [ 1.3455; 1.3478] 0.3 1.5 Southern Europe
## PUERTO RICO 13.2279 [13.2214; 13.2344] 0.9 1.5 Central and South America and the Caribbean
## ROMANIA 1.1118 [ 1.1110; 1.1125] 0.4 1.5 Eastern Europe
## RUSSIA 0.0930 [ 0.0930; 0.0931] 0.3 1.5 Eastern Europe
## SAUDI ARABIA 0.4083 [ 0.4079; 0.4087] 0.3 1.5 Western Asia
## SERBIA 0.1056 [ 0.1052; 0.1060] 0.0 1.5 Southern Europe
## SLOVAKIA 1.4980 [ 1.4963; 1.4997] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3248 [ 0.3235; 0.3261] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0642 [ 0.0640; 0.0643] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.8851 [ 0.8847; 0.8855] 0.9 1.5 Eastern Asia
## SPAIN 2.0645 [ 2.0638; 2.0652] 1.9 1.5 Southern Europe
## SWEDEN 3.4050 [ 3.4031; 3.4069] 0.7 1.5 Northern Europe
## SWITZERLAND 0.5020 [ 0.5012; 0.5028] 0.1 1.5 Western Europe
## TAIWAN 0.2339 [ 0.2335; 0.2342] 0.1 1.5 Eastern Asia
## THAILAND 0.5326 [ 0.5324; 0.5329] 0.7 1.5 South-eastern Asia
## TUNISIA 0.0939 [ 0.0936; 0.0942] 0.0 1.5 Northern Africa
## TÜRKIYE 2.4956 [ 2.4950; 2.4961] 3.9 1.5 Western Asia
## UNITED ARAB EMIRATES 0.0726 [ 0.0723; 0.0729] 0.0 1.5 Western Asia
## UNITED KINGDOM 5.7608 [ 5.7598; 5.7617] 7.5 1.5 Northern Europe
## UNITED STATES 10.2970 [10.2964; 10.2975] 65.3 1.5 Northern America
## URUGUAY 0.3608 [ 0.3598; 0.3619] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.0491 [ 0.0490; 0.0493] 0.0 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.2903 [5.2900; 5.2905] 71831.16 0
## Random effects model 0.3651 [0.2427; 0.5491] -4.84 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.8207 [1.8569; 5.9271]; tau = 1.6795 [1.3627; 2.4346]
## I^2 = 100.0%; H = 6788.80
##
## Test of heterogeneity:
## Q d.f. p-value
## 2949619479.48 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 9.9058 [9.9053; 9.9064] 38450605.61 100.0%
## Region = Central and South America and t ... 10 1.0458 [1.0455; 1.0462] 198560580.21 100.0%
## Region = Northern Europe 8 5.0676 [5.0669; 5.0684] 19099065.99 100.0%
## Region = Eastern Europe 8 0.7082 [0.7080; 0.7085] 34331254.58 100.0%
## Region = Southern Europe 8 1.3656 [1.3652; 1.3660] 20997743.55 100.0%
## Region = Western Europe 7 1.4638 [1.4635; 1.4641] 6776855.64 100.0%
## Region = Australia and New Zealand 2 1.4662 [1.4653; 1.4670] 7813323.91 100.0%
## Region = Eastern Asia 4 0.1750 [0.1749; 0.1750] 94218049.76 100.0%
## Region = Central Asia 1 0.0254 [0.0253; 0.0255] 0.00 --
## Region = South-eastern Asia 2 0.4303 [0.4301; 0.4305] 8485068.19 100.0%
## Region = Southern Asia 2 0.0827 [0.0827; 0.0827] 1936935.94 100.0%
## Region = Western Asia 6 2.1041 [2.1037; 2.1046] 25194165.00 100.0%
## Region = Northern Africa 4 0.1236 [0.1235; 0.1237] 1207406.76 100.0%
## Region = Southern Africa 1 0.0642 [0.0640; 0.0643] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2492548424.34 13 0
## Within groups 457071055.14 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 6.8430 [3.0720; 15.2430] 0.3340 0.5779
## Region = Central and South America and t ... 10 0.1102 [0.0176; 0.6899] 8.7607 2.9598
## Region = Northern Europe 8 1.7542 [1.1264; 2.7318] 0.4086 0.6392
## Region = Eastern Europe 8 0.4815 [0.2235; 1.0374] 1.2270 1.1077
## Region = Southern Europe 8 0.4617 [0.2654; 0.8030] 0.6380 0.7988
## Region = Western Europe 7 1.0227 [0.8012; 1.3054] 0.1085 0.3294
## Region = Australia and New Zealand 2 1.4601 [0.2870; 7.4289] 1.3780 1.1739
## Region = Eastern Asia 4 0.1455 [0.0195; 1.0886] 4.2167 2.0535
## Region = Central Asia 1 0.0254 [0.0253; 0.0255] -- --
## Region = South-eastern Asia 2 0.1210 [0.0066; 2.2101] 4.3942 2.0962
## Region = Southern Asia 2 0.0564 [0.0240; 0.1325] 0.3803 0.6167
## Region = Western Asia 6 0.3282 [0.1090; 0.9884] 1.8984 1.3778
## Region = Northern Africa 4 0.0846 [0.0469; 0.1527] 0.3634 0.6028
## Region = Southern Africa 1 0.0642 [0.0640; 0.0643] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 127140.85 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.1275 [ 0.1273; 0.1277] 0.1 1.5 Northern Africa
## ARGENTINA 0.1241 [ 0.1240; 0.1243] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.6583 [ 0.6578; 0.6589] 0.3 1.5 Australia and New Zealand
## AUSTRIA 2.0880 [ 2.0864; 2.0896] 0.3 1.5 Western Europe
## BELARUS 0.0843 [ 0.0840; 0.0846] 0.0 1.5 Eastern Europe
## BELGIUM 0.9062 [ 0.9053; 0.9071] 0.2 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.3009 [ 0.3000; 0.3019] 0.0 1.5 Southern Europe
## BRAZIL 0.1148 [ 0.1148; 0.1149] 0.4 1.5 Central and South America and the Caribbean
## BULGARIA 0.5224 [ 0.5215; 0.5233] 0.1 1.5 Eastern Europe
## CANADA 4.7693 [ 4.7681; 4.7705] 3.1 1.5 Northern America
## CHILE 0.0198 [ 0.0197; 0.0200] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0293 [ 0.0293; 0.0293] 0.7 1.5 Eastern Asia
## COLOMBIA 0.0119 [ 0.0118; 0.0119] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1282 [ 0.1276; 0.1288] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.0591 [ 2.0577; 2.0606] 0.4 1.5 Eastern Europe
## ECUADOR 0.0520 [ 0.0519; 0.0522] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1489 [ 0.1488; 0.1491] 0.3 1.5 Northern Africa
## ESTONIA 1.1053 [ 1.1024; 1.1083] 0.0 1.5 Northern Europe
## FINLAND 2.6654 [ 2.6631; 2.6676] 0.3 1.5 Northern Europe
## FRANCE 1.3419 [ 1.3415; 1.3424] 1.5 1.5 Western Europe
## GERMANY 1.6769 [ 1.6765; 1.6774] 2.5 1.5 Western Europe
## GREECE 1.0026 [ 1.0016; 1.0036] 0.2 1.5 Southern Europe
## HUNGARY 0.7477 [ 0.7468; 0.7486] 0.1 1.5 Eastern Europe
## INDIA 0.0989 [ 0.0989; 0.0989] 2.4 1.5 Southern Asia
## IRELAND 1.1919 [ 1.1903; 1.1936] 0.1 1.5 Northern Europe
## ITALY 0.5694 [ 0.5691; 0.5698] 0.6 1.5 Southern Europe
## JAPAN 0.0856 [ 0.0855; 0.0857] 0.2 1.5 Eastern Asia
## JORDAN 0.1192 [ 0.1189; 0.1196] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0230 [ 0.0229; 0.0231] 0.0 1.5 Central Asia
## KUWAIT 0.9078 [ 0.9062; 0.9093] 0.1 1.5 Western Asia
## LATVIA 2.0008 [ 1.9975; 2.0041] 0.1 1.5 Northern Europe
## LEBANON 0.4970 [ 0.4962; 0.4979] 0.1 1.5 Western Asia
## LITHUANIA 0.5211 [ 0.5197; 0.5225] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6295 [ 0.6261; 0.6328] 0.0 1.5 Western Europe
## MEXICO 0.0994 [ 0.0993; 0.0995] 0.2 1.5 Central and South America and the Caribbean
## MOROCCO 0.0369 [ 0.0368; 0.0370] 0.0 1.5 Northern Africa
## NETHERLANDS 0.7887 [ 0.7880; 0.7894] 0.2 1.5 Western Europe
## NEW ZEALAND 3.7529 [ 3.7500; 3.7558] 0.3 1.5 Australia and New Zealand
## NORWAY 2.9508 [ 2.9484; 2.9532] 0.3 1.5 Northern Europe
## PAKISTAN 0.0311 [ 0.0310; 0.0311] 0.1 1.5 Southern Asia
## PERU 0.0733 [ 0.0732; 0.0735] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0243 [ 0.0242; 0.0243] 0.0 1.5 South-eastern Asia
## POLAND 0.4682 [ 0.4678; 0.4685] 0.3 1.5 Eastern Europe
## PORTUGAL 1.4084 [ 1.4072; 1.4096] 0.3 1.5 Southern Europe
## PUERTO RICO 14.8281 [14.8211; 14.8351] 0.8 1.5 Central and South America and the Caribbean
## ROMANIA 1.3080 [ 1.3072; 1.3089] 0.5 1.5 Eastern Europe
## RUSSIA 0.1532 [ 0.1531; 0.1533] 0.4 1.5 Eastern Europe
## SAUDI ARABIA 0.5669 [ 0.5665; 0.5673] 0.3 1.5 Western Asia
## SERBIA 0.1059 [ 0.1055; 0.1062] 0.0 1.5 Southern Europe
## SLOVAKIA 1.5627 [ 1.5609; 1.5644] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3402 [ 0.3389; 0.3415] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0655 [ 0.0654; 0.0656] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.9343 [ 0.9339; 0.9348] 0.9 1.5 Eastern Asia
## SPAIN 2.1655 [ 2.1648; 2.1662] 1.8 1.5 Southern Europe
## SWEDEN 3.9049 [ 3.9029; 3.9070] 0.7 1.5 Northern Europe
## SWITZERLAND 0.5067 [ 0.5059; 0.5075] 0.1 1.5 Western Europe
## TAIWAN 0.2404 [ 0.2401; 0.2407] 0.1 1.5 Eastern Asia
## THAILAND 0.6236 [ 0.6233; 0.6239] 0.8 1.5 South-eastern Asia
## TUNISIA 0.0915 [ 0.0912; 0.0918] 0.0 1.5 Northern Africa
## TÜRKIYE 2.5773 [ 2.5767; 2.5779] 3.7 1.5 Western Asia
## UNITED ARAB EMIRATES 0.0593 [ 0.0590; 0.0595] 0.0 1.5 Western Asia
## UNITED KINGDOM 6.1083 [ 6.1073; 6.1093] 7.3 1.5 Northern Europe
## UNITED STATES 11.4514 [11.4508; 11.4520] 66.3 1.5 Northern America
## URUGUAY 0.3376 [ 0.3365; 0.3386] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.0634 [ 0.0633; 0.0636] 0.0 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.8461 [5.8459; 5.8464] 79960.47 0
## Random effects model 0.3929 [0.2587; 0.5968] -4.38 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.9569 [1.8636; 5.9664]; tau = 1.7196 [1.3651; 2.4426]
## I^2 = 100.0%; H = 7221.33
##
## Test of heterogeneity:
## Q d.f. p-value
## 3337446034.08 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 11.0094 [11.0088; 11.0099] 46885844.31 100.0%
## Region = Central and South America and t ... 10 1.1795 [ 1.1791; 1.1799] 217721506.58 100.0%
## Region = Northern Europe 8 5.3963 [ 5.3955; 5.3971] 18935583.40 100.0%
## Region = Eastern Europe 8 0.7213 [ 0.7211; 0.7215] 36237051.22 100.0%
## Region = Southern Europe 8 1.4283 [ 1.4280; 1.4287] 22421508.04 100.0%
## Region = Western Europe 7 1.4622 [ 1.4619; 1.4625] 6626712.57 100.0%
## Region = Australia and New Zealand 2 1.6320 [ 1.6311; 1.6329] 9341458.86 100.0%
## Region = Eastern Asia 4 0.1743 [ 0.1742; 0.1744] 99749180.39 100.0%
## Region = Central Asia 1 0.0230 [ 0.0229; 0.0231] 0.00 --
## Region = South-eastern Asia 2 0.5202 [ 0.5200; 0.5205] 9276525.80 100.0%
## Region = Southern Asia 2 0.0937 [ 0.0937; 0.0937] 3017502.47 100.0%
## Region = Western Asia 6 2.1441 [ 2.1437; 2.1446] 24556919.29 100.0%
## Region = Northern Africa 4 0.1290 [ 0.1289; 0.1291] 904429.41 100.0%
## Region = Southern Africa 1 0.0655 [ 0.0654; 0.0656] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2841771811.74 13 0
## Within groups 495674222.34 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 7.3902 [3.1323; 17.4360] 0.3836 0.6194
## Region = Central and South America and t ... 10 0.1150 [0.0176; 0.7530] 9.1947 3.0323
## Region = Northern Europe 8 2.0017 [1.3233; 3.0279] 0.3567 0.5973
## Region = Eastern Europe 8 0.5619 [0.2699; 1.1698] 1.1200 1.0583
## Region = Southern Europe 8 0.4709 [0.2676; 0.8287] 0.6652 0.8156
## Region = Western Europe 7 1.0099 [0.7938; 1.2847] 0.1056 0.3250
## Region = Australia and New Zealand 2 1.5718 [0.2855; 8.6535] 1.5148 1.2308
## Region = Eastern Asia 4 0.1541 [0.0212; 1.1194] 4.0955 2.0237
## Region = Central Asia 1 0.0230 [0.0229; 0.0231] -- --
## Region = South-eastern Asia 2 0.1230 [0.0051; 2.9621] 5.2691 2.2954
## Region = Southern Asia 2 0.0554 [0.0178; 0.1725] 0.6709 0.8191
## Region = Western Asia 6 0.4087 [0.1621; 1.0305] 1.3362 1.1559
## Region = Northern Africa 4 0.0895 [0.0565; 0.1418] 0.2205 0.4696
## Region = Southern Africa 1 0.0655 [0.0654; 0.0656] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 150746.52 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0899 [ 0.0898; 0.0901] 0.1 1.5 Northern Africa
## ARGENTINA 0.1258 [ 0.1257; 0.1260] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.7003 [ 0.6998; 0.7009] 0.3 1.5 Australia and New Zealand
## AUSTRIA 2.0631 [ 2.0615; 2.0646] 0.3 1.5 Western Europe
## BELARUS 0.1204 [ 0.1200; 0.1208] 0.0 1.5 Eastern Europe
## BELGIUM 0.8770 [ 0.8761; 0.8779] 0.2 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.3650 [ 0.3640; 0.3661] 0.0 1.5 Southern Europe
## BRAZIL 0.1201 [ 0.1201; 0.1202] 0.4 1.5 Central and South America and the Caribbean
## BULGARIA 0.4317 [ 0.4309; 0.4325] 0.1 1.5 Eastern Europe
## CANADA 4.8245 [ 4.8233; 4.8256] 3.1 1.5 Northern America
## CHILE 0.0214 [ 0.0213; 0.0215] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0330 [ 0.0329; 0.0330] 0.8 1.5 Eastern Asia
## COLOMBIA 0.0146 [ 0.0145; 0.0146] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1217 [ 0.1211; 0.1223] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.0989 [ 2.0974; 2.1003] 0.4 1.5 Eastern Europe
## ECUADOR 0.0512 [ 0.0510; 0.0513] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1647 [ 0.1646; 0.1648] 0.3 1.5 Northern Africa
## ESTONIA 1.2831 [ 1.2799; 1.2862] 0.0 1.5 Northern Europe
## FINLAND 3.0971 [ 3.0947; 3.0996] 0.3 1.5 Northern Europe
## FRANCE 1.3516 [ 1.3511; 1.3521] 1.5 1.5 Western Europe
## GERMANY 1.6751 [ 1.6746; 1.6755] 2.4 1.5 Western Europe
## GREECE 0.9888 [ 0.9878; 0.9898] 0.2 1.5 Southern Europe
## HUNGARY 0.8107 [ 0.8098; 0.8117] 0.1 1.5 Eastern Europe
## INDIA 0.1112 [ 0.1112; 0.1112] 2.6 1.5 Southern Asia
## IRELAND 1.2545 [ 1.2528; 1.2562] 0.1 1.5 Northern Europe
## ITALY 0.5734 [ 0.5731; 0.5737] 0.6 1.5 Southern Europe
## JAPAN 0.0833 [ 0.0832; 0.0833] 0.2 1.5 Eastern Asia
## JORDAN 0.3329 [ 0.3323; 0.3335] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0263 [ 0.0262; 0.0265] 0.0 1.5 Central Asia
## KUWAIT 0.9165 [ 0.9150; 0.9180] 0.1 1.5 Western Asia
## LATVIA 2.3533 [ 2.3498; 2.3569] 0.1 1.5 Northern Europe
## LEBANON 0.4987 [ 0.4978; 0.4996] 0.1 1.5 Western Asia
## LITHUANIA 0.5957 [ 0.5942; 0.5972] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6434 [ 0.6400; 0.6467] 0.0 1.5 Western Europe
## MEXICO 0.1003 [ 0.1002; 0.1004] 0.2 1.5 Central and South America and the Caribbean
## MOROCCO 0.0402 [ 0.0401; 0.0403] 0.0 1.5 Northern Africa
## NETHERLANDS 0.8129 [ 0.8122; 0.8136] 0.2 1.5 Western Europe
## NEW ZEALAND 3.9910 [ 3.9880; 3.9939] 0.3 1.5 Australia and New Zealand
## NORWAY 3.1948 [ 3.1923; 3.1973] 0.3 1.5 Northern Europe
## PAKISTAN 0.0328 [ 0.0328; 0.0328] 0.1 1.5 Southern Asia
## PERU 0.0703 [ 0.0701; 0.0704] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0299 [ 0.0298; 0.0299] 0.1 1.5 South-eastern Asia
## POLAND 0.5082 [ 0.5079; 0.5086] 0.3 1.5 Eastern Europe
## PORTUGAL 1.4806 [ 1.4794; 1.4819] 0.3 1.5 Southern Europe
## PUERTO RICO 13.9247 [13.9178; 13.9317] 0.7 1.5 Central and South America and the Caribbean
## ROMANIA 1.3918 [ 1.3909; 1.3926] 0.5 1.5 Eastern Europe
## RUSSIA 0.2102 [ 0.2101; 0.2103] 0.5 1.5 Eastern Europe
## SAUDI ARABIA 0.7810 [ 0.7805; 0.7815] 0.5 1.5 Western Asia
## SERBIA 0.1066 [ 0.1063; 0.1070] 0.0 1.5 Southern Europe
## SLOVAKIA 1.7118 [ 1.7100; 1.7136] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3713 [ 0.3700; 0.3727] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0662 [ 0.0661; 0.0663] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.9307 [ 0.9303; 0.9312] 0.8 1.5 Eastern Asia
## SPAIN 2.2219 [ 2.2212; 2.2226] 1.8 1.5 Southern Europe
## SWEDEN 4.4354 [ 4.4332; 4.4376] 0.8 1.5 Northern Europe
## SWITZERLAND 0.5148 [ 0.5141; 0.5156] 0.1 1.5 Western Europe
## TAIWAN 0.2588 [ 0.2585; 0.2592] 0.1 1.5 Eastern Asia
## THAILAND 0.7063 [ 0.7060; 0.7067] 0.8 1.5 South-eastern Asia
## TUNISIA 0.0909 [ 0.0906; 0.0911] 0.0 1.5 Northern Africa
## TÜRKIYE 2.5175 [ 2.5170; 2.5181] 3.6 1.5 Western Asia
## UNITED ARAB EMIRATES 0.0566 [ 0.0563; 0.0568] 0.0 1.5 Western Asia
## UNITED KINGDOM 6.1017 [ 6.1008; 6.1027] 7.1 1.5 Northern Europe
## UNITED STATES 11.6873 [11.6867; 11.6879] 66.1 1.5 Northern America
## URUGUAY 0.3050 [ 0.3040; 0.3059] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.0258 [ 0.0257; 0.0259] 0.0 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.8486 [5.8484; 5.8489] 81200.80 0
## Random effects model 0.4135 [0.2713; 0.6301] -4.11 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 3.0022 [1.8266; 5.8085]; tau = 1.7327 [1.3515; 2.4101]
## I^2 = 100.0%; H = 7411.30
##
## Test of heterogeneity:
## Q d.f. p-value
## 3515352636.41 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 11.2341 [11.2335; 11.2347] 48859543.91 100.0%
## Region = Central and South America and t ... 10 1.0035 [ 1.0031; 1.0038] 201395877.31 100.0%
## Region = Northern Europe 8 5.4451 [ 5.4443; 5.4459] 16583184.93 100.0%
## Region = Eastern Europe 8 0.7371 [ 0.7369; 0.7373] 35675580.48 100.0%
## Region = Southern Europe 8 1.4668 [ 1.4665; 1.4672] 23217524.82 100.0%
## Region = Western Europe 7 1.4629 [ 1.4627; 1.4632] 6481148.91 100.0%
## Region = Australia and New Zealand 2 1.7326 [ 1.7317; 1.7335] 10039724.38 100.0%
## Region = Eastern Asia 4 0.1682 [ 0.1681; 0.1682] 98898236.56 100.0%
## Region = Central Asia 1 0.0263 [ 0.0262; 0.0265] 0.00 --
## Region = South-eastern Asia 2 0.5824 [ 0.5822; 0.5827] 10930833.68 100.0%
## Region = Southern Asia 2 0.1054 [ 0.1053; 0.1054] 3622537.00 100.0%
## Region = Western Asia 6 2.0610 [ 2.0606; 2.0614] 22109284.06 100.0%
## Region = Northern Africa 4 0.1321 [ 0.1320; 0.1322] 1295162.86 100.0%
## Region = Southern Africa 1 0.0662 [ 0.0661; 0.0663] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 3036243997.51 13 0
## Within groups 479108638.90 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 7.5090 [3.1551; 17.8712] 0.3914 0.6257
## Region = Central and South America and t ... 10 0.1064 [0.0171; 0.6640] 8.7267 2.9541
## Region = Northern Europe 8 2.2266 [1.5437; 3.2116] 0.2794 0.5286
## Region = Eastern Europe 8 0.6224 [0.3121; 1.2412] 0.9925 0.9962
## Region = Southern Europe 8 0.4892 [0.2767; 0.8649] 0.6760 0.8222
## Region = Western Europe 7 1.0141 [0.7999; 1.2857] 0.1026 0.3204
## Region = Australia and New Zealand 2 1.6718 [0.3038; 9.2011] 1.5142 1.2305
## Region = Eastern Asia 4 0.1604 [0.0233; 1.1043] 3.8770 1.9690
## Region = Central Asia 1 0.0263 [0.0262; 0.0265] -- --
## Region = South-eastern Asia 2 0.1452 [0.0065; 3.2249] 5.0047 2.2371
## Region = Southern Asia 2 0.0604 [0.0183; 0.1998] 0.7452 0.8633
## Region = Western Asia 6 0.5067 [0.2356; 1.0897] 0.9159 0.9570
## Region = Northern Africa 4 0.0858 [0.0479; 0.1535] 0.3529 0.5940
## Region = Southern Africa 1 0.0662 [0.0661; 0.0663] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 134259.14 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0000 0.0 0.0 Eastern Asia
## PUERTO RICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## UNITED STATES 0.0000 0.0 0.0 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 0
##
## rate 95%-CI z p-value
## Common effect model NA -- --
## Random effects model NA -- --
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 0 NA -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 0 NA -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups NA 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 0 NA -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 0 NA -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2009 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0000 0.0 0.0 Eastern Asia
## PUERTO RICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## UNITED STATES 0.0000 0.0 0.0 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 0
##
## rate 95%-CI z p-value
## Common effect model NA -- --
## Random effects model NA -- --
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 0 NA -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 0 NA -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups NA 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 0 NA -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 0 NA -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2010 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0000 0.0 0.0 Eastern Asia
## PUERTO RICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## UNITED STATES 0.0000 0.0 0.0 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 0
##
## rate 95%-CI z p-value
## Common effect model NA -- --
## Random effects model NA -- --
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 0 NA -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 0 NA -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups NA 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 0 NA -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 0 NA -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2011 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0000 0.0 0.0 Eastern Asia
## PUERTO RICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## UNITED STATES 0.0039 [0.0039; 0.0039] 100.0 100.0 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 1
##
## rate 95%-CI z p-value
## Common effect model 0.0039 [0.0039; 0.0039] -3694.30 0
## Random effects model 0.0039 [0.0039; 0.0039] -3694.30 0
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 1 0.0039 [0.0039; 0.0039] 0.00 --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 0 NA -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 1 0.0039 [0.0039; 0.0039] -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 0 NA -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2012 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0094 [0.0094; 0.0095] 22.4 35.7 Eastern Asia
## PUERTO RICO 0.0001 [0.0000; 0.0001] 0.0 28.6 Central and South America and the Caribbean
## UNITED STATES 0.0133 [0.0133; 0.0134] 77.6 35.7 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 3
##
## rate 95%-CI z p-value
## Common effect model 0.0124 [0.0123; 0.0124] -6174.23 0
## Random effects model 0.0024 [0.0018; 0.0032] -40.32 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0626 [0.0659; 53.3307]; tau = 0.2503 [0.2567; 7.3028]
## I^2 = 100.0%; H = 146.72
##
## Test of heterogeneity:
## Q d.f. p-value
## 43054.43 2 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 1 0.0133 [0.0133; 0.0134] 0.00 --
## Region = Central and South America and t ... 1 0.0001 [0.0000; 0.0001] 0.00 --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0094 [0.0094; 0.0095] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 43054.43 2 0
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 1 0.0133 [0.0133; 0.0134] -- --
## Region = Central and South America and t ... 1 0.0001 [0.0000; 0.0001] -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0094 [0.0094; 0.0095] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 43054.43 2 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0313 [0.0313; 0.0314] 50.9 33.5 Eastern Asia
## PUERTO RICO 0.0001 [0.0001; 0.0001] 0.0 33.0 Central and South America and the Caribbean
## UNITED STATES 0.0122 [0.0122; 0.0123] 49.1 33.5 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 3
##
## rate 95%-CI z p-value
## Common effect model 0.0198 [0.0197; 0.0198] -6663.61 0
## Random effects model 0.0037 [0.0017; 0.0078] -14.53 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.4448 [0.1404; >100.0000]; tau = 0.6669 [0.3747; >10.0000]
## I^2 = 100.0%; H = 566.14
##
## Test of heterogeneity:
## Q d.f. p-value
## 641032.17 2 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 1 0.0122 [0.0122; 0.0123] 0.00 --
## Region = Central and South America and t ... 1 0.0001 [0.0001; 0.0001] 0.00 --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0313 [0.0313; 0.0314] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 641032.17 2 0
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 1 0.0122 [0.0122; 0.0123] -- --
## Region = Central and South America and t ... 1 0.0001 [0.0001; 0.0001] -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0313 [0.0313; 0.0314] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 641032.17 2 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0440 [0.0439; 0.0441] 44.0 50.0 Eastern Asia
## PUERTO RICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## UNITED STATES 0.0225 [0.0225; 0.0226] 56.0 50.0 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0302 [0.0302; 0.0303] -7570.26 0
## Random effects model 0.0315 [0.0163; 0.0607] -10.34 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.2239; tau = 0.4731; I^2 = 100.0%; H = 718.72
##
## Test of heterogeneity:
## Q d.f. p-value
## 516556.17 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 1 0.0225 [0.0225; 0.0226] 0.00 --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0440 [0.0439; 0.0441] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 516556.17 1 0
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 1 0.0225 [0.0225; 0.0226] -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0440 [0.0439; 0.0441] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 516556.17 1 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2015 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0502 [0.0502; 0.0503] 36.2 35.1 Eastern Asia
## PUERTO RICO 0.0001 [0.0001; 0.0001] 0.0 29.8 Central and South America and the Caribbean
## UNITED STATES 0.0353 [0.0353; 0.0353] 63.8 35.1 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 3
##
## rate 95%-CI z p-value
## Common effect model 0.0401 [0.0401; 0.0401] -8191.00 0
## Random effects model 0.0063 [0.0047; 0.0085] -33.91 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.0634 [0.0549; 57.4238]; tau = 0.2518 [0.2343; 7.5778]
## I^2 = 100.0%; H = 308.20
##
## Test of heterogeneity:
## Q d.f. p-value
## 189978.76 2 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 1 0.0353 [0.0353; 0.0353] 0.00 --
## Region = Central and South America and t ... 1 0.0001 [0.0001; 0.0001] 0.00 --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0502 [0.0502; 0.0503] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 189978.76 2 0
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 1 0.0353 [0.0353; 0.0353] -- --
## Region = Central and South America and t ... 1 0.0001 [0.0001; 0.0001] -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0502 [0.0502; 0.0503] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 189978.76 2 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0542 [0.0542; 0.0543] 29.8 50.0 Eastern Asia
## PUERTO RICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## UNITED STATES 0.0505 [0.0505; 0.0506] 70.2 50.0 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0516 [0.0516; 0.0516] -8638.01 0
## Random effects model 0.0523 [0.0489; 0.0561] -83.96 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0025; tau = 0.0497; I^2 = 100.0%; H = 93.66
##
## Test of heterogeneity:
## Q d.f. p-value
## 8771.96 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 1 0.0505 [0.0505; 0.0506] 0.00 --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0542 [0.0542; 0.0543] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 8771.96 1 0
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 1 0.0505 [0.0505; 0.0506] -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0542 [0.0542; 0.0543] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 8771.96 1 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2017 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0581 [0.0580; 0.0582] 29.7 50.0 Eastern Asia
## PUERTO RICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## UNITED STATES 0.0540 [0.0539; 0.0540] 70.3 50.0 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0552 [0.0551; 0.0552] -8747.31 0
## Random effects model 0.0560 [0.0521; 0.0602] -78.44 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0027; tau = 0.0520; I^2 = 100.0%; H = 101.38
##
## Test of heterogeneity:
## Q d.f. p-value
## 10276.94 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 1 0.0540 [0.0539; 0.0540] 0.00 --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0581 [0.0580; 0.0582] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 10276.94 1 0
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 1 0.0540 [0.0539; 0.0540] -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0581 [0.0580; 0.0582] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 10276.94 1 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2018 2
## rate 95%-CI %W(common) %W(random) Region
## JAPAN 0.0635 [0.0634; 0.0636] 34.4 50.0 Eastern Asia
## PUERTO RICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## UNITED STATES 0.0470 [0.0470; 0.0471] 65.6 50.0 Northern America
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.0000 0.0 0.0 Central and South America and the Caribbean
## AUSTRALIA 0.0000 0.0 0.0 Australia and New Zealand
## AUSTRIA 0.0000 0.0 0.0 Western Europe
## BELARUS 0.0000 0.0 0.0 Eastern Europe
## BELGIUM 0.0000 0.0 0.0 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0000 0.0 0.0 Eastern Europe
## CANADA 0.0000 0.0 0.0 Northern America
## CHILE 0.0000 0.0 0.0 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0000 0.0 0.0 Central and South America and the Caribbean
## CROATIA 0.0000 0.0 0.0 Southern Europe
## CZECH REPUBLIC 0.0000 0.0 0.0 Eastern Europe
## ECUADOR 0.0000 0.0 0.0 Central and South America and the Caribbean
## EGYPT 0.0000 0.0 0.0 Northern Africa
## ESTONIA 0.0000 0.0 0.0 Northern Europe
## FINLAND 0.0000 0.0 0.0 Northern Europe
## FRANCE 0.0000 0.0 0.0 Western Europe
## GERMANY 0.0000 0.0 0.0 Western Europe
## GREECE 0.0000 0.0 0.0 Southern Europe
## HUNGARY 0.0000 0.0 0.0 Eastern Europe
## INDIA 0.0000 0.0 0.0 Southern Asia
## IRELAND 0.0000 0.0 0.0 Northern Europe
## ITALY 0.0000 0.0 0.0 Southern Europe
## JORDAN 0.0000 0.0 0.0 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0000 0.0 0.0 Western Asia
## LATVIA 0.0000 0.0 0.0 Northern Europe
## LEBANON 0.0000 0.0 0.0 Western Asia
## LITHUANIA 0.0000 0.0 0.0 Northern Europe
## LUXEMBOURG 0.0000 0.0 0.0 Western Europe
## MEXICO 0.0000 0.0 0.0 Central and South America and the Caribbean
## MOROCCO 0.0000 0.0 0.0 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0000 0.0 0.0 Australia and New Zealand
## NORWAY 0.0000 0.0 0.0 Northern Europe
## PAKISTAN 0.0000 0.0 0.0 Southern Asia
## PERU 0.0000 0.0 0.0 Central and South America and the Caribbean
## PHILIPPINES 0.0000 0.0 0.0 South-eastern Asia
## POLAND 0.0000 0.0 0.0 Eastern Europe
## PORTUGAL 0.0000 0.0 0.0 Southern Europe
## ROMANIA 0.0000 0.0 0.0 Eastern Europe
## RUSSIA 0.0000 0.0 0.0 Eastern Europe
## SAUDI ARABIA 0.0000 0.0 0.0 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.0000 0.0 0.0 Eastern Europe
## SLOVENIA 0.0000 0.0 0.0 Southern Europe
## SOUTH AFRICA 0.0000 0.0 0.0 Southern Africa
## SOUTH KOREA 0.0000 0.0 0.0 Eastern Asia
## SPAIN 0.0000 0.0 0.0 Southern Europe
## SWEDEN 0.0000 0.0 0.0 Northern Europe
## SWITZERLAND 0.0000 0.0 0.0 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0000 0.0 0.0 Northern Africa
## TÜRKIYE 0.0000 0.0 0.0 Western Asia
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 Western Asia
## UNITED KINGDOM 0.0000 0.0 0.0 Northern Europe
## URUGUAY 0.0000 0.0 0.0 Central and South America and the Caribbean
## VENEZUELA 0.0000 0.0 0.0 Central and South America and the Caribbean
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0521 [0.0521; 0.0522] -8645.90 0
## Random effects model 0.0546 [0.0407; 0.0733] -19.39 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.0450; tau = 0.2121; I^2 = 100.0%; H = 417.10
##
## Test of heterogeneity:
## Q d.f. p-value
## 173973.45 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 1 0.0470 [0.0470; 0.0471] 0.00 --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0635 [0.0634; 0.0636] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 173973.45 1 0
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 1 0.0470 [0.0470; 0.0471] -- --
## Region = Central and South America and t ... 0 NA -- --
## Region = Northern Europe 0 NA -- --
## Region = Eastern Europe 0 NA -- --
## Region = Southern Europe 0 NA -- --
## Region = Western Europe 0 NA -- --
## Region = Australia and New Zealand 0 NA -- --
## Region = Eastern Asia 1 0.0635 [0.0634; 0.0636] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 0 NA -- --
## Region = Southern Asia 0 NA -- --
## Region = Western Asia 0 NA -- --
## Region = Northern Africa 0 NA -- --
## Region = Southern Africa 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 173973.45 1 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2008 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0709 [0.0708; 0.0711] 0.2 1.8 Northern Africa
## ARGENTINA 0.1063 [0.1061; 0.1065] 0.3 1.8 Central and South America and the Caribbean
## AUSTRALIA 0.5278 [0.5272; 0.5283] 0.7 1.8 Australia and New Zealand
## AUSTRIA 0.7538 [0.7528; 0.7548] 0.4 1.8 Western Europe
## BELARUS 0.0007 [0.0007; 0.0008] 0.0 1.8 Eastern Europe
## BELGIUM 0.9523 [0.9513; 0.9533] 0.7 1.8 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0186 [0.0185; 0.0188] 0.0 1.8 Eastern Europe
## CANADA 1.6387 [1.6380; 1.6395] 3.5 1.8 Northern America
## CHILE 0.1689 [0.1685; 0.1692] 0.2 1.8 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0536 [0.0535; 0.0537] 0.2 1.8 Central and South America and the Caribbean
## CROATIA 0.0233 [0.0231; 0.0236] 0.0 1.8 Southern Europe
## CZECH REPUBLIC 0.4263 [0.4256; 0.4269] 0.3 1.8 Eastern Europe
## ECUADOR 0.1220 [0.1217; 0.1223] 0.1 1.8 Central and South America and the Caribbean
## EGYPT 0.0305 [0.0305; 0.0306] 0.2 1.8 Northern Africa
## ESTONIA 0.1266 [0.1256; 0.1276] 0.0 1.8 Northern Europe
## FINLAND 3.3965 [3.3939; 3.3991] 1.1 1.8 Northern Europe
## FRANCE 2.1673 [2.1667; 2.1679] 8.6 1.8 Western Europe
## GERMANY 1.5554 [1.5549; 1.5558] 8.0 1.8 Western Europe
## GREECE 1.4149 [1.4138; 1.4161] 1.0 1.8 Southern Europe
## HUNGARY 0.4953 [0.4946; 0.4961] 0.3 1.8 Eastern Europe
## INDIA 0.0834 [0.0834; 0.0835] 6.4 1.8 Southern Asia
## IRELAND 1.9534 [1.9512; 1.9556] 0.5 1.8 Northern Europe
## ITALY 0.9209 [0.9205; 0.9213] 3.4 1.8 Southern Europe
## JAPAN 0.0000 0.0 0.0 Eastern Asia
## JORDAN 0.0510 [0.0507; 0.0513] 0.0 1.8 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0877 [0.0871; 0.0883] 0.0 1.8 Western Asia
## LATVIA 0.0843 [0.0837; 0.0849] 0.0 1.8 Northern Europe
## LEBANON 0.1605 [0.1599; 0.1611] 0.0 1.8 Western Asia
## LITHUANIA 0.1282 [0.1276; 0.1289] 0.0 1.8 Northern Europe
## LUXEMBOURG 2.3963 [2.3891; 2.4035] 0.1 1.8 Western Europe
## MEXICO 0.2075 [0.2074; 0.2077] 1.5 1.8 Central and South America and the Caribbean
## MOROCCO 0.0162 [0.0161; 0.0163] 0.0 1.8 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0120 [0.0118; 0.0121] 0.0 1.8 Australia and New Zealand
## NORWAY 2.3244 [2.3221; 2.3267] 0.7 1.8 Northern Europe
## PAKISTAN 0.0431 [0.0430; 0.0431] 0.5 1.8 Southern Asia
## PERU 0.0255 [0.0254; 0.0256] 0.0 1.8 Central and South America and the Caribbean
## PHILIPPINES 0.0270 [0.0269; 0.0270] 0.2 1.8 South-eastern Asia
## POLAND 0.0043 [0.0043; 0.0043] 0.0 1.8 Eastern Europe
## PORTUGAL 1.8127 [1.8114; 1.8140] 1.2 1.8 Southern Europe
## PUERTO RICO 1.2632 [1.2613; 1.2651] 0.3 1.8 Central and South America and the Caribbean
## ROMANIA 0.2512 [0.2508; 0.2515] 0.3 1.8 Eastern Europe
## RUSSIA 0.0137 [0.0137; 0.0138] 0.1 1.8 Eastern Europe
## SAUDI ARABIA 0.2557 [0.2553; 0.2560] 0.4 1.8 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.8130 [0.8117; 0.8143] 0.3 1.8 Eastern Europe
## SLOVENIA 1.0760 [1.0736; 1.0784] 0.1 1.8 Southern Europe
## SOUTH AFRICA 0.0644 [0.0643; 0.0645] 0.2 1.8 Southern Africa
## SOUTH KOREA 0.3052 [0.3049; 0.3055] 1.0 1.8 Eastern Asia
## SPAIN 2.0490 [2.0484; 2.0497] 6.0 1.8 Southern Europe
## SWEDEN 2.1903 [2.1887; 2.1919] 1.3 1.8 Northern Europe
## SWITZERLAND 1.3323 [1.3310; 1.3337] 0.6 1.8 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0578 [0.0575; 0.0580] 0.0 1.8 Northern Africa
## TÜRKIYE 0.2052 [0.2050; 0.2053] 0.9 1.8 Western Asia
## UNITED ARAB EMIRATES 0.2101 [0.2095; 0.2106] 0.1 1.8 Western Asia
## UNITED KINGDOM 1.3586 [1.3581; 1.3591] 5.4 1.8 Northern Europe
## UNITED STATES 2.1871 [2.1868; 2.1873] 42.2 1.8 Northern America
## URUGUAY 0.0582 [0.0578; 0.0586] 0.0 1.8 Central and South America and the Caribbean
## VENEZUELA 0.2297 [0.2294; 0.2300] 0.4 1.8 Central and South America and the Caribbean
##
## Number of studies combined: k = 56
##
## rate 95%-CI z p-value
## Common effect model 1.2863 [1.2862; 1.2864] 6034.53 0
## Random effects model 0.2112 [0.1564; 0.2852] -10.15 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.3158 [1.2226; 3.7999]; tau = 1.1471 [1.1057; 1.9493]
## I^2 = 100.0%; H = 3303.27
##
## Test of heterogeneity:
## Q d.f. p-value
## 600138502.72 55 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.1396 [2.1393; 2.1398] 1535997.93 100.0%
## Region = Central and South America and t ... 9 0.2054 [0.2053; 0.2055] 9246969.51 100.0%
## Region = Northern Europe 8 1.7157 [1.7152; 1.7162] 7664667.23 100.0%
## Region = Eastern Europe 8 0.3041 [0.3039; 0.3043] 10716904.43 100.0%
## Region = Southern Europe 6 1.5372 [1.5368; 1.5375] 9033186.47 100.0%
## Region = Western Europe 6 1.7501 [1.7498; 1.7505] 6227429.60 100.0%
## Region = Australia and New Zealand 2 0.5188 [0.5183; 0.5193] 266695.99 100.0%
## Region = Eastern Asia 1 0.3052 [0.3049; 0.3055] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0270 [0.0269; 0.0270] 0.00 --
## Region = Southern Asia 2 0.0797 [0.0797; 0.0797] 1098267.67 100.0%
## Region = Western Asia 6 0.2107 [0.2106; 0.2109] 425970.07 100.0%
## Region = Northern Africa 4 0.0436 [0.0435; 0.0436] 526000.68 100.0%
## Region = Southern Africa 1 0.0644 [0.0643; 0.0645] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 553396413.13 12 0
## Within groups 46742089.59 43 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 1.8932 [1.4267; 2.5121] 0.0417 0.2041
## Region = Central and South America and t ... 9 0.1298 [0.0729; 0.2312] 0.7796 0.8830
## Region = Northern Europe 8 0.7075 [0.5034; 0.9946] 0.2414 0.4914
## Region = Eastern Europe 8 0.0494 [0.0198; 0.1232] 1.7419 1.3198
## Region = Southern Europe 6 0.7039 [0.4885; 1.0143] 0.2085 0.4566
## Region = Western Europe 6 1.4060 [1.0912; 1.8116] 0.1003 0.3168
## Region = Australia and New Zealand 2 0.0794 [0.0019; 3.2509] 7.1735 2.6783
## Region = Eastern Asia 1 0.3052 [0.3049; 0.3055] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0270 [0.0269; 0.0270] -- --
## Region = Southern Asia 2 0.0600 [0.0314; 0.1146] 0.2183 0.4672
## Region = Western Asia 6 0.1412 [0.1113; 0.1791] 0.0884 0.2973
## Region = Northern Africa 4 0.0377 [0.0208; 0.0684] 0.3684 0.6069
## Region = Southern Africa 1 0.0644 [0.0643; 0.0645] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 5993104.49 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.2753 [0.2750; 0.2756] 0.5 1.8 Northern Africa
## ARGENTINA 0.1721 [0.1719; 0.1723] 0.4 1.8 Central and South America and the Caribbean
## AUSTRALIA 0.7022 [0.7016; 0.7028] 0.8 1.8 Australia and New Zealand
## AUSTRIA 0.9630 [0.9619; 0.9641] 0.4 1.8 Western Europe
## BELARUS 0.0048 [0.0047; 0.0049] 0.0 1.8 Eastern Europe
## BELGIUM 1.2113 [1.2103; 1.2124] 0.7 1.8 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0149 [0.0149; 0.0149] 0.2 1.8 Central and South America and the Caribbean
## BULGARIA 0.1057 [0.1053; 0.1061] 0.0 1.8 Eastern Europe
## CANADA 2.1523 [2.1515; 2.1531] 3.9 1.8 Northern America
## CHILE 0.1928 [0.1924; 0.1931] 0.2 1.8 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0553 [0.0552; 0.0554] 0.1 1.8 Central and South America and the Caribbean
## CROATIA 0.0632 [0.0628; 0.0636] 0.0 1.8 Southern Europe
## CZECH REPUBLIC 0.5902 [0.5894; 0.5909] 0.3 1.8 Eastern Europe
## ECUADOR 0.1724 [0.1721; 0.1728] 0.1 1.8 Central and South America and the Caribbean
## EGYPT 0.0666 [0.0665; 0.0667] 0.3 1.8 Northern Africa
## ESTONIA 0.1453 [0.1442; 0.1463] 0.0 1.8 Northern Europe
## FINLAND 4.0030 [4.0002; 4.0058] 1.1 1.8 Northern Europe
## FRANCE 2.5553 [2.5547; 2.5560] 8.6 1.8 Western Europe
## GERMANY 1.8744 [1.8739; 1.8749] 8.1 1.8 Western Europe
## GREECE 1.8254 [1.8241; 1.8267] 1.1 1.8 Southern Europe
## HUNGARY 0.5623 [0.5615; 0.5631] 0.3 1.8 Eastern Europe
## INDIA 0.1226 [0.1226; 0.1226] 8.0 1.8 Southern Asia
## IRELAND 2.6032 [2.6008; 2.6057] 0.6 1.8 Northern Europe
## ITALY 1.1183 [1.1179; 1.1188] 3.5 1.8 Southern Europe
## JAPAN 0.0000 0.0 0.0 Eastern Asia
## JORDAN 0.0665 [0.0662; 0.0669] 0.0 1.8 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1249 [0.1242; 0.1256] 0.0 1.8 Western Asia
## LATVIA 0.1286 [0.1278; 0.1294] 0.0 1.8 Northern Europe
## LEBANON 0.2201 [0.2194; 0.2208] 0.1 1.8 Western Asia
## LITHUANIA 0.1815 [0.1807; 0.1823] 0.0 1.8 Northern Europe
## LUXEMBOURG 2.6071 [2.5997; 2.6145] 0.1 1.8 Western Europe
## MEXICO 0.2079 [0.2077; 0.2080] 1.3 1.8 Central and South America and the Caribbean
## MOROCCO 0.0335 [0.0334; 0.0336] 0.1 1.8 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0279 [0.0276; 0.0281] 0.0 1.8 Australia and New Zealand
## NORWAY 2.1852 [2.1830; 2.1873] 0.6 1.8 Northern Europe
## PAKISTAN 0.0910 [0.0909; 0.0911] 0.9 1.8 Southern Asia
## PERU 0.0375 [0.0374; 0.0376] 0.1 1.8 Central and South America and the Caribbean
## PHILIPPINES 0.0372 [0.0371; 0.0372] 0.2 1.8 South-eastern Asia
## POLAND 0.0071 [0.0070; 0.0071] 0.0 1.8 Eastern Europe
## PORTUGAL 2.2539 [2.2525; 2.2554] 1.3 1.8 Southern Europe
## PUERTO RICO 1.2524 [1.2505; 1.2543] 0.2 1.8 Central and South America and the Caribbean
## ROMANIA 0.4295 [0.4290; 0.4300] 0.5 1.8 Eastern Europe
## RUSSIA 0.0284 [0.0284; 0.0285] 0.2 1.8 Eastern Europe
## SAUDI ARABIA 0.3084 [0.3081; 0.3088] 0.4 1.8 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 1.1281 [1.1266; 1.1296] 0.3 1.8 Eastern Europe
## SLOVENIA 1.3641 [1.3615; 1.3668] 0.1 1.8 Southern Europe
## SOUTH AFRICA 0.1130 [0.1129; 0.1132] 0.3 1.8 Southern Africa
## SOUTH KOREA 0.4698 [0.4694; 0.4701] 1.2 1.8 Eastern Asia
## SPAIN 2.6137 [2.6129; 2.6145] 6.5 1.8 Southern Europe
## SWEDEN 2.7350 [2.7332; 2.7368] 1.4 1.8 Northern Europe
## SWITZERLAND 1.5463 [1.5449; 1.5478] 0.6 1.8 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.1176 [0.1172; 0.1179] 0.1 1.8 Northern Africa
## TÜRKIYE 0.2955 [0.2953; 0.2957] 1.1 1.8 Western Asia
## UNITED ARAB EMIRATES 0.3381 [0.3374; 0.3387] 0.1 1.8 Western Asia
## UNITED KINGDOM 1.8136 [1.8130; 1.8141] 6.1 1.8 Northern Europe
## UNITED STATES 2.2201 [2.2199; 2.2204] 36.4 1.8 Northern America
## URUGUAY 0.1756 [0.1748; 0.1763] 0.0 1.8 Central and South America and the Caribbean
## VENEZUELA 0.2484 [0.2480; 0.2487] 0.4 1.8 Central and South America and the Caribbean
##
## Number of studies combined: k = 57
##
## rate 95%-CI z p-value
## Common effect model 1.3437 [1.3436; 1.3438] 7718.17 0
## Random effects model 0.3024 [0.2252; 0.4060] -7.96 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2880 [1.1678; 3.4536]; tau = 1.1349 [1.0806; 1.8584]
## I^2 = 100.0%; H = 3620.06
##
## Test of heterogeneity:
## Q d.f. p-value
## 733870312.71 56 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.2135 [2.2132; 2.2138] 23087.78 100.0%
## Region = Central and South America and t ... 10 0.1878 [0.1877; 0.1879] 15387580.19 100.0%
## Region = Northern Europe 8 2.1468 [2.1462; 2.1473] 7515737.61 100.0%
## Region = Eastern Europe 8 0.3774 [0.3772; 0.3777] 15799897.69 100.0%
## Region = Southern Europe 6 1.9427 [1.9423; 1.9432] 12801029.89 100.0%
## Region = Western Europe 6 2.0833 [2.0829; 2.0836] 6629979.07 100.0%
## Region = Australia and New Zealand 2 0.6847 [0.6841; 0.6853] 454510.50 100.0%
## Region = Eastern Asia 1 0.4698 [0.4694; 0.4701] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0372 [0.0371; 0.0372] 0.00 --
## Region = Southern Asia 2 0.1191 [0.1191; 0.1192] 468870.47 100.0%
## Region = Western Asia 6 0.2904 [0.2902; 0.2906] 520895.36 100.0%
## Region = Northern Africa 4 0.1467 [0.1466; 0.1468] 3512657.89 100.0%
## Region = Southern Africa 1 0.1130 [0.1129; 0.1132] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 670756066.26 12 0
## Within groups 63114246.46 44 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.1860 [2.1205; 2.2535] 0.0005 0.0219
## Region = Central and South America and t ... 10 0.1349 [0.0725; 0.2513] 1.0058 1.0029
## Region = Northern Europe 8 0.8870 [0.6538; 1.2033] 0.1937 0.4402
## Region = Eastern Europe 8 0.1064 [0.0432; 0.2619] 1.6912 1.3005
## Region = Southern Europe 6 1.0061 [0.6824; 1.4833] 0.2354 0.4852
## Region = Western Europe 6 1.6805 [1.3241; 2.1330] 0.0888 0.2979
## Region = Australia and New Zealand 2 0.1399 [0.0059; 3.3051] 5.2073 2.2820
## Region = Eastern Asia 1 0.4698 [0.4694; 0.4701] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0372 [0.0371; 0.0372] -- --
## Region = Southern Asia 2 0.1056 [0.0788; 0.1415] 0.0445 0.2109
## Region = Western Asia 6 0.1958 [0.1568; 0.2446] 0.0773 0.2780
## Region = Northern Africa 4 0.0922 [0.0356; 0.2389] 0.9445 0.9719
## Region = Southern Africa 1 0.1130 [0.1129; 0.1132] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 9115765.81 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.3584 [0.3581; 0.3587] 0.6 1.7 Northern Africa
## ARGENTINA 0.2832 [0.2829; 0.2835] 0.5 1.7 Central and South America and the Caribbean
## AUSTRALIA 0.8531 [0.8524; 0.8537] 0.9 1.7 Australia and New Zealand
## AUSTRIA 1.2971 [1.2959; 1.2984] 0.5 1.7 Western Europe
## BELARUS 0.0057 [0.0056; 0.0058] 0.0 1.7 Eastern Europe
## BELGIUM 1.7617 [1.7604; 1.7630] 0.9 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0598 [0.0597; 0.0598] 0.5 1.7 Central and South America and the Caribbean
## BULGARIA 0.1647 [0.1642; 0.1652] 0.1 1.7 Eastern Europe
## CANADA 2.5654 [2.5645; 2.5663] 4.1 1.7 Northern America
## CHILE 0.2310 [0.2307; 0.2314] 0.2 1.7 Central and South America and the Caribbean
## CHINA 0.0000 [0.0000; 0.0000] 0.0 1.7 Eastern Asia
## COLOMBIA 0.0525 [0.0524; 0.0526] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.1874 [0.1868; 0.1881] 0.0 1.7 Southern Europe
## CZECH REPUBLIC 0.7723 [0.7714; 0.7731] 0.4 1.7 Eastern Europe
## ECUADOR 0.2196 [0.2193; 0.2200] 0.2 1.7 Central and South America and the Caribbean
## EGYPT 0.1329 [0.1327; 0.1330] 0.5 1.7 Northern Africa
## ESTONIA 0.1949 [0.1936; 0.1961] 0.0 1.7 Northern Europe
## FINLAND 4.4528 [4.4499; 4.4558] 1.1 1.7 Northern Europe
## FRANCE 2.9223 [2.9216; 2.9230] 8.5 1.7 Western Europe
## GERMANY 2.1889 [2.1883; 2.1894] 8.2 1.7 Western Europe
## GREECE 2.0220 [2.0206; 2.0234] 1.0 1.7 Southern Europe
## HUNGARY 0.6597 [0.6589; 0.6605] 0.3 1.7 Eastern Europe
## INDIA 0.1545 [0.1545; 0.1546] 8.9 1.7 Southern Asia
## IRELAND 3.0900 [3.0873; 3.0926] 0.7 1.7 Northern Europe
## ITALY 1.3033 [1.3028; 1.3037] 3.6 1.7 Southern Europe
## JAPAN 0.1340 [0.1339; 0.1341] 0.8 1.7 Eastern Asia
## JORDAN 0.0748 [0.0744; 0.0751] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1322 [0.1315; 0.1329] 0.0 1.7 Western Asia
## LATVIA 0.0932 [0.0925; 0.0939] 0.0 1.7 Northern Europe
## LEBANON 0.2822 [0.2814; 0.2830] 0.1 1.7 Western Asia
## LITHUANIA 0.2761 [0.2751; 0.2770] 0.0 1.7 Northern Europe
## LUXEMBOURG 2.8977 [2.8900; 2.9055] 0.1 1.7 Western Europe
## MEXICO 0.2123 [0.2122; 0.2124] 1.1 1.7 Central and South America and the Caribbean
## MOROCCO 0.0372 [0.0371; 0.0373] 0.1 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0433 [0.0429; 0.0436] 0.0 1.7 Australia and New Zealand
## NORWAY 2.1474 [2.1452; 2.1495] 0.5 1.7 Northern Europe
## PAKISTAN 0.1127 [0.1127; 0.1128] 0.9 1.7 Southern Asia
## PERU 0.0528 [0.0527; 0.0530] 0.1 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0396 [0.0395; 0.0397] 0.2 1.7 South-eastern Asia
## POLAND 0.0090 [0.0089; 0.0090] 0.0 1.7 Eastern Europe
## PORTUGAL 2.6513 [2.6496; 2.6529] 1.3 1.7 Southern Europe
## PUERTO RICO 1.2272 [1.2253; 1.2291] 0.2 1.7 Central and South America and the Caribbean
## ROMANIA 0.3411 [0.3407; 0.3415] 0.3 1.7 Eastern Europe
## RUSSIA 0.0725 [0.0724; 0.0725] 0.5 1.7 Eastern Europe
## SAUDI ARABIA 0.3637 [0.3633; 0.3641] 0.5 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 1.4980 [1.4963; 1.4997] 0.4 1.7 Eastern Europe
## SLOVENIA 1.6411 [1.6382; 1.6440] 0.2 1.7 Southern Europe
## SOUTH AFRICA 0.1369 [0.1367; 0.1371] 0.3 1.7 Southern Africa
## SOUTH KOREA 0.5801 [0.5797; 0.5804] 1.3 1.7 Eastern Asia
## SPAIN 3.0765 [3.0757; 3.0773] 6.7 1.7 Southern Europe
## SWEDEN 3.0492 [3.0473; 3.0510] 1.3 1.7 Northern Europe
## SWITZERLAND 1.8991 [1.8975; 1.9007] 0.7 1.7 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0669 [0.0668; 0.0670] 0.2 1.7 South-eastern Asia
## TUNISIA 0.1599 [0.1595; 0.1603] 0.1 1.7 Northern Africa
## TÜRKIYE 0.3880 [0.3878; 0.3883] 1.3 1.7 Western Asia
## UNITED ARAB EMIRATES 0.6148 [0.6140; 0.6157] 0.2 1.7 Western Asia
## UNITED KINGDOM 2.3866 [2.3860; 2.3873] 7.0 1.7 Northern Europe
## UNITED STATES 2.1808 [2.1805; 2.1811] 31.3 1.7 Northern America
## URUGUAY 0.3082 [0.3073; 0.3092] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.2893 [0.2890; 0.2896] 0.4 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 1.3708 [1.3707; 1.3709] 8842.94 0
## Random effects model 0.3166 [0.2367; 0.4235] -7.75 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.3219 [1.1966; 3.3619]; tau = 1.1498 [1.0939; 1.8336]
## I^2 = 100.0%; H = 3905.31
##
## Test of heterogeneity:
## Q d.f. p-value
## 899835081.38 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.2219 [2.2217; 2.2222] 746916.15 100.0%
## Region = Central and South America and t ... 10 0.1958 [0.1957; 0.1959] 15110283.34 100.0%
## Region = Northern Europe 8 2.6183 [2.6177; 2.6188] 6506270.94 100.0%
## Region = Eastern Europe 8 0.3797 [0.3795; 0.3799] 20664536.04 100.0%
## Region = Southern Europe 6 2.2675 [2.2670; 2.2679] 15896797.72 100.0%
## Region = Western Europe 6 2.4238 [2.4235; 2.4242] 5632704.75 100.0%
## Region = Australia and New Zealand 2 0.8282 [0.8276; 0.8289] 607742.82 100.0%
## Region = Eastern Asia 3 0.3322 [0.3320; 0.3323] 9759332.54 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.0528 [0.0527; 0.0528] 204889.73 100.0%
## Region = Southern Asia 2 0.1499 [0.1499; 0.1500] 663938.28 100.0%
## Region = Western Asia 6 0.3881 [0.3879; 0.3883] 1178972.07 100.0%
## Region = Northern Africa 4 0.2047 [0.2045; 0.2048] 3543122.42 100.0%
## Region = Southern Africa 1 0.1369 [0.1367; 0.1371] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 819319574.60 12 0
## Within groups 80515506.78 47 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.3653 [2.0172; 2.7734] 0.0132 0.1148
## Region = Central and South America and t ... 10 0.1881 [0.1118; 0.3164] 0.7042 0.8392
## Region = Northern Europe 8 1.0095 [0.7752; 1.3145] 0.1452 0.3811
## Region = Eastern Europe 8 0.1414 [0.0577; 0.3464] 1.6734 1.2936
## Region = Southern Europe 6 1.3700 [0.9183; 2.0439] 0.2500 0.5000
## Region = Western Europe 6 2.0777 [1.7002; 2.5390] 0.0628 0.2506
## Region = Australia and New Zealand 2 0.1921 [0.0103; 3.5687] 4.4450 2.1083
## Region = Eastern Asia 3 0.0133 [0.0038; 0.0469] 1.2367 1.1121
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.0515 [0.0308; 0.0861] 0.1377 0.3711
## Region = Southern Asia 2 0.1320 [0.0969; 0.1798] 0.0497 0.2229
## Region = Western Asia 6 0.2496 [0.1878; 0.3318] 0.1264 0.3556
## Region = Northern Africa 4 0.1297 [0.0604; 0.2788] 0.6096 0.7808
## Region = Southern Africa 1 0.1369 [0.1367; 0.1371] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 2330.57 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.5023 [0.5019; 0.5026] 0.7 1.6 Northern Africa
## ARGENTINA 0.4871 [0.4868; 0.4875] 0.8 1.6 Central and South America and the Caribbean
## AUSTRALIA 1.0422 [1.0415; 1.0429] 0.9 1.6 Australia and New Zealand
## AUSTRIA 1.6069 [1.6055; 1.6083] 0.5 1.6 Western Europe
## BELARUS 0.0049 [0.0049; 0.0050] 0.0 1.6 Eastern Europe
## BELGIUM 2.1459 [2.1444; 2.1473] 0.9 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.0047 [0.0046; 0.0048] 0.0 1.6 Southern Europe
## BRAZIL 0.0849 [0.0848; 0.0849] 0.7 1.6 Central and South America and the Caribbean
## BULGARIA 0.2681 [0.2675; 0.2687] 0.1 1.6 Eastern Europe
## CANADA 2.8489 [2.8480; 2.8498] 3.8 1.6 Northern America
## CHILE 0.2907 [0.2903; 0.2912] 0.2 1.6 Central and South America and the Caribbean
## CHINA 0.0006 [0.0006; 0.0006] 0.0 1.6 Eastern Asia
## COLOMBIA 0.0601 [0.0600; 0.0602] 0.1 1.6 Central and South America and the Caribbean
## CROATIA 0.2490 [0.2483; 0.2498] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.0325 [1.0315; 1.0335] 0.4 1.6 Eastern Europe
## ECUADOR 0.2650 [0.2645; 0.2654] 0.2 1.6 Central and South America and the Caribbean
## EGYPT 0.2101 [0.2100; 0.2103] 0.7 1.6 Northern Africa
## ESTONIA 0.2309 [0.2295; 0.2322] 0.0 1.6 Northern Europe
## FINLAND 4.7455 [4.7425; 4.7485] 1.0 1.6 Northern Europe
## FRANCE 3.1118 [3.1111; 3.1125] 7.7 1.6 Western Europe
## GERMANY 2.3903 [2.3897; 2.3909] 7.5 1.6 Western Europe
## GREECE 2.1845 [2.1831; 2.1860] 0.9 1.6 Southern Europe
## HUNGARY 0.7808 [0.7799; 0.7817] 0.3 1.6 Eastern Europe
## INDIA 0.1927 [0.1926; 0.1927] 9.4 1.6 Southern Asia
## IRELAND 3.7947 [3.7917; 3.7976] 0.7 1.6 Northern Europe
## ITALY 1.4936 [1.4931; 1.4942] 3.5 1.6 Southern Europe
## JAPAN 0.9483 [0.9481; 0.9486] 4.8 1.6 Eastern Asia
## JORDAN 0.0969 [0.0965; 0.0972] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0096 [0.0095; 0.0097] 0.0 1.6 Central Asia
## KUWAIT 0.1977 [0.1969; 0.1985] 0.0 1.6 Western Asia
## LATVIA 0.0698 [0.0692; 0.0703] 0.0 1.6 Northern Europe
## LEBANON 0.4274 [0.4265; 0.4283] 0.1 1.6 Western Asia
## LITHUANIA 0.4855 [0.4842; 0.4868] 0.1 1.6 Northern Europe
## LUXEMBOURG 3.0533 [3.0455; 3.0612] 0.1 1.6 Western Europe
## MEXICO 0.1896 [0.1894; 0.1897] 0.9 1.6 Central and South America and the Caribbean
## MOROCCO 0.0429 [0.0428; 0.0430] 0.1 1.6 Northern Africa
## NETHERLANDS 1.8776 [1.8765; 1.8787] 1.2 1.6 Western Europe
## NEW ZEALAND 0.0559 [0.0555; 0.0562] 0.0 1.6 Australia and New Zealand
## NORWAY 2.4446 [2.4424; 2.4469] 0.5 1.6 Northern Europe
## PAKISTAN 0.1482 [0.1481; 0.1483] 1.1 1.6 Southern Asia
## PERU 0.0621 [0.0619; 0.0622] 0.1 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0455 [0.0455; 0.0456] 0.2 1.6 South-eastern Asia
## POLAND 0.0169 [0.0169; 0.0170] 0.0 1.6 Eastern Europe
## PORTUGAL 2.7607 [2.7590; 2.7623] 1.1 1.6 Southern Europe
## PUERTO RICO 1.2731 [1.2712; 1.2750] 0.2 1.6 Central and South America and the Caribbean
## ROMANIA 0.3141 [0.3137; 0.3145] 0.2 1.6 Eastern Europe
## RUSSIA 0.1415 [0.1414; 0.1416] 0.8 1.6 Eastern Europe
## SAUDI ARABIA 0.4245 [0.4241; 0.4249] 0.5 1.6 Western Asia
## SERBIA 0.0287 [0.0285; 0.0289] 0.0 1.6 Southern Europe
## SLOVAKIA 2.0042 [2.0022; 2.0062] 0.4 1.6 Eastern Europe
## SLOVENIA 1.8618 [1.8587; 1.8649] 0.1 1.6 Southern Europe
## SOUTH AFRICA 0.1591 [0.1589; 0.1592] 0.3 1.6 Southern Africa
## SOUTH KOREA 0.6952 [0.6948; 0.6956] 1.4 1.6 Eastern Asia
## SPAIN 3.5436 [3.5427; 3.5445] 6.5 1.6 Southern Europe
## SWEDEN 3.2395 [3.2376; 3.2413] 1.2 1.6 Northern Europe
## SWITZERLAND 2.1983 [2.1966; 2.2000] 0.7 1.6 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.1031 [0.1030; 0.1033] 0.3 1.6 South-eastern Asia
## TUNISIA 0.1955 [0.1950; 0.1959] 0.1 1.6 Northern Africa
## TÜRKIYE 0.5242 [0.5240; 0.5245] 1.5 1.6 Western Asia
## UNITED ARAB EMIRATES 0.6845 [0.6836; 0.6854] 0.2 1.6 Western Asia
## UNITED KINGDOM 2.9403 [2.9396; 2.9409] 7.3 1.6 Northern Europe
## UNITED STATES 2.1866 [2.1863; 2.1868] 26.6 1.6 Northern America
## URUGUAY 0.3948 [0.3937; 0.3959] 0.1 1.6 Central and South America and the Caribbean
## VENEZUELA 0.3710 [0.3706; 0.3713] 0.4 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 1.4264 [1.4263; 1.4265] 10868.38 0
## Random effects model 0.3605 [0.2762; 0.4705] -7.51 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1812 [1.1393; 3.0693]; tau = 1.0868 [1.0674; 1.7519]
## I^2 = 100.0%; H = 3958.38
##
## Test of heterogeneity:
## Q d.f. p-value
## 987134380.25 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.2608 [2.2606; 2.2611] 2198773.01 100.0%
## Region = Central and South America and t ... 10 0.2390 [0.2390; 0.2391] 19435163.65 100.0%
## Region = Northern Europe 8 3.0866 [3.0860; 3.0872] 5817963.39 100.0%
## Region = Eastern Europe 8 0.4525 [0.4523; 0.4527] 25771185.22 100.0%
## Region = Southern Europe 8 2.5580 [2.5576; 2.5585] 20575019.80 100.0%
## Region = Western Europe 7 2.5736 [2.5732; 2.5740] 5678033.59 100.0%
## Region = Australia and New Zealand 2 1.0110 [1.0103; 1.0117] 763923.00 100.0%
## Region = Eastern Asia 3 0.8538 [0.8536; 0.8541] 16206503.66 100.0%
## Region = Central Asia 1 0.0096 [0.0095; 0.0097] 0.00 --
## Region = South-eastern Asia 2 0.0753 [0.0752; 0.0754] 653532.18 100.0%
## Region = Southern Asia 2 0.1876 [0.1876; 0.1877] 613472.78 100.0%
## Region = Western Asia 6 0.4970 [0.4968; 0.4972] 1315843.83 100.0%
## Region = Northern Africa 4 0.2965 [0.2963; 0.2966] 4690223.83 100.0%
## Region = Southern Africa 1 0.1591 [0.1589; 0.1592] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 883414742.32 13 0
## Within groups 103719637.93 50 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.4959 [1.9258; 3.2347] 0.0350 0.1871
## Region = Central and South America and t ... 10 0.2303 [0.1360; 0.3899] 0.7213 0.8493
## Region = Northern Europe 8 1.1601 [0.9184; 1.4654] 0.1137 0.3372
## Region = Eastern Europe 8 0.1887 [0.0800; 0.4450] 1.5324 1.2379
## Region = Southern Europe 8 0.4602 [0.3172; 0.6677] 0.2883 0.5370
## Region = Western Europe 7 2.2830 [1.9361; 2.6920] 0.0495 0.2225
## Region = Australia and New Zealand 2 0.2413 [0.0137; 4.2460] 4.2818 2.0692
## Region = Eastern Asia 3 0.0719 [0.0261; 0.1982] 0.8040 0.8967
## Region = Central Asia 1 0.0096 [0.0095; 0.0097] -- --
## Region = South-eastern Asia 2 0.0685 [0.0308; 0.1527] 0.3340 0.5780
## Region = Southern Asia 2 0.1690 [0.1307; 0.2185] 0.0344 0.1854
## Region = Western Asia 6 0.3281 [0.2513; 0.4282] 0.1109 0.3330
## Region = Northern Africa 4 0.1725 [0.0830; 0.3585] 0.5577 0.7468
## Region = Southern Africa 1 0.1591 [0.1589; 0.1592] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 449063.62 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.5436 [0.5432; 0.5440] 0.7 1.5 Northern Africa
## ARGENTINA 0.6692 [0.6688; 0.6696] 1.0 1.5 Central and South America and the Caribbean
## AUSTRALIA 1.1969 [1.1962; 1.1977] 0.9 1.5 Australia and New Zealand
## AUSTRIA 1.9266 [1.9250; 1.9281] 0.6 1.5 Western Europe
## BELARUS 0.0061 [0.0060; 0.0062] 0.0 1.5 Eastern Europe
## BELGIUM 2.2474 [2.2459; 2.2489] 0.9 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0084 [0.0082; 0.0085] 0.0 1.5 Southern Europe
## BRAZIL 0.1127 [0.1126; 0.1128] 0.8 1.5 Central and South America and the Caribbean
## BULGARIA 0.0647 [0.0644; 0.0650] 0.0 1.5 Eastern Europe
## CANADA 3.1446 [3.1436; 3.1456] 3.8 1.5 Northern America
## CHILE 0.3808 [0.3804; 0.3813] 0.2 1.5 Central and South America and the Caribbean
## CHINA 0.0017 [0.0017; 0.0017] 0.1 1.5 Eastern Asia
## COLOMBIA 0.0711 [0.0710; 0.0712] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.3248 [0.3239; 0.3257] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.1044 [1.1034; 1.1055] 0.4 1.5 Eastern Europe
## ECUADOR 0.3125 [0.3120; 0.3129] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 0.3476 [0.3474; 0.3478] 1.0 1.5 Northern Africa
## ESTONIA 0.2598 [0.2584; 0.2613] 0.0 1.5 Northern Europe
## FINLAND 5.0579 [5.0548; 5.0610] 0.9 1.5 Northern Europe
## FRANCE 3.4227 [3.4219; 3.4234] 7.5 1.5 Western Europe
## GERMANY 2.6294 [2.6288; 2.6300] 7.3 1.5 Western Europe
## GREECE 2.1222 [2.1208; 2.1237] 0.8 1.5 Southern Europe
## HUNGARY 0.9178 [0.9168; 0.9188] 0.3 1.5 Eastern Europe
## INDIA 0.2189 [0.2188; 0.2189] 9.5 1.5 Southern Asia
## IRELAND 4.8273 [4.8240; 4.8306] 0.8 1.5 Northern Europe
## ITALY 1.6178 [1.6172; 1.6183] 3.3 1.5 Southern Europe
## JAPAN 1.6550 [1.6546; 1.6554] 7.3 1.5 Eastern Asia
## JORDAN 0.1713 [0.1709; 0.1718] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0219 [0.0218; 0.0220] 0.0 1.5 Central Asia
## KUWAIT 0.3129 [0.3119; 0.3139] 0.0 1.5 Western Asia
## LATVIA 0.0739 [0.0733; 0.0745] 0.0 1.5 Northern Europe
## LEBANON 0.4941 [0.4931; 0.4951] 0.1 1.5 Western Asia
## LITHUANIA 0.5840 [0.5826; 0.5855] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.3108 [3.3027; 3.3189] 0.1 1.5 Western Europe
## MEXICO 0.1834 [0.1833; 0.1835] 0.7 1.5 Central and South America and the Caribbean
## MOROCCO 0.0481 [0.0479; 0.0482] 0.1 1.5 Northern Africa
## NETHERLANDS 2.1870 [2.1858; 2.1881] 1.3 1.5 Western Europe
## NEW ZEALAND 0.0606 [0.0602; 0.0610] 0.0 1.5 Australia and New Zealand
## NORWAY 2.6430 [2.6406; 2.6454] 0.5 1.5 Northern Europe
## PAKISTAN 0.1655 [0.1654; 0.1656] 1.1 1.5 Southern Asia
## PERU 0.0764 [0.0762; 0.0766] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0513 [0.0512; 0.0513] 0.2 1.5 South-eastern Asia
## POLAND 0.0124 [0.0124; 0.0125] 0.0 1.5 Eastern Europe
## PORTUGAL 3.1609 [3.1591; 3.1627] 1.1 1.5 Southern Europe
## PUERTO RICO 1.2744 [1.2724; 1.2763] 0.2 1.5 Central and South America and the Caribbean
## ROMANIA 0.2308 [0.2305; 0.2312] 0.2 1.5 Eastern Europe
## RUSSIA 0.2983 [0.2982; 0.2985] 1.5 1.5 Eastern Europe
## SAUDI ARABIA 0.6099 [0.6094; 0.6103] 0.6 1.5 Western Asia
## SERBIA 0.0514 [0.0512; 0.0517] 0.0 1.5 Southern Europe
## SLOVAKIA 2.1634 [2.1614; 2.1655] 0.4 1.5 Eastern Europe
## SLOVENIA 2.0907 [2.0875; 2.0940] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.1847 [0.1845; 0.1849] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.0076 [1.0072; 1.0081] 1.7 1.5 Eastern Asia
## SPAIN 3.9095 [3.9085; 3.9104] 6.3 1.5 Southern Europe
## SWEDEN 3.2022 [3.2003; 3.2040] 1.0 1.5 Northern Europe
## SWITZERLAND 2.4428 [2.4410; 2.4445] 0.7 1.5 Western Europe
## TAIWAN 0.0082 [0.0082; 0.0083] 0.0 1.5 Eastern Asia
## THAILAND 0.1173 [0.1172; 0.1175] 0.3 1.5 South-eastern Asia
## TUNISIA 0.2953 [0.2948; 0.2959] 0.1 1.5 Northern Africa
## TÜRKIYE 0.7695 [0.7692; 0.7699] 2.0 1.5 Western Asia
## UNITED ARAB EMIRATES 0.2766 [0.2760; 0.2772] 0.1 1.5 Western Asia
## UNITED KINGDOM 3.4816 [3.4808; 3.4823] 7.7 1.5 Northern Europe
## UNITED STATES 2.0969 [2.0967; 2.0972] 22.6 1.5 Northern America
## URUGUAY 0.6758 [0.6743; 0.6772] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.4633 [0.4629; 0.4637] 0.5 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.5211 [1.5210; 1.5212] 13684.51 0
## Random effects model 0.4013 [0.3103; 0.5191] -6.95 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1201 [1.1273; 3.0014]; tau = 1.0583 [1.0617; 1.7325]
## I^2 = 100.0%; H = 4114.94
##
## Test of heterogeneity:
## Q d.f. p-value
## 1083692664.03 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.2220 [2.2217; 2.2222] 5644374.52 100.0%
## Region = Central and South America and t ... 10 0.2948 [0.2948; 0.2949] 25414502.83 100.0%
## Region = Northern Europe 8 3.5544 [3.5538; 3.5551] 6403590.09 100.0%
## Region = Eastern Europe 8 0.5198 [0.5196; 0.5200] 21741294.72 100.0%
## Region = Southern Europe 8 2.7975 [2.7970; 2.7980] 24509070.32 100.0%
## Region = Western Europe 7 2.8386 [2.8382; 2.8390] 5721498.51 100.0%
## Region = Australia and New Zealand 2 1.1625 [1.1618; 1.1632] 871567.40 100.0%
## Region = Eastern Asia 4 1.4099 [1.4097; 1.4102] 45374333.24 100.0%
## Region = Central Asia 1 0.0219 [0.0218; 0.0220] 0.00 --
## Region = South-eastern Asia 2 0.0853 [0.0852; 0.0854] 767198.47 100.0%
## Region = Southern Asia 2 0.2128 [0.2128; 0.2129] 796178.07 100.0%
## Region = Western Asia 6 0.6743 [0.6740; 0.6745] 2437697.91 100.0%
## Region = Northern Africa 4 0.3833 [0.3832; 0.3835] 3606048.41 100.0%
## Region = Southern Africa 1 0.1847 [0.1845; 0.1849] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 940405309.54 13 0
## Within groups 143287354.48 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.5679 [1.7263; 3.8197] 0.0821 0.2865
## Region = Central and South America and t ... 10 0.2853 [0.1657; 0.4910] 0.7678 0.8763
## Region = Northern Europe 8 1.2983 [1.0287; 1.6384] 0.1128 0.3358
## Region = Eastern Europe 8 0.1713 [0.0826; 0.3554] 1.1091 1.0531
## Region = Southern Europe 8 0.5776 [0.3913; 0.8527] 0.3159 0.5621
## Region = Western Europe 7 2.5438 [2.1748; 2.9754] 0.0448 0.2116
## Region = Australia and New Zealand 2 0.2693 [0.0145; 5.0114] 4.4501 2.1095
## Region = Eastern Asia 4 0.0699 [0.0215; 0.2272] 1.4464 1.2027
## Region = Central Asia 1 0.0219 [0.0218; 0.0220] -- --
## Region = South-eastern Asia 2 0.0776 [0.0345; 0.1746] 0.3427 0.5854
## Region = Southern Asia 2 0.1903 [0.1447; 0.2503] 0.0391 0.1977
## Region = Western Asia 6 0.3885 [0.2792; 0.5407] 0.1706 0.4131
## Region = Northern Africa 4 0.2276 [0.1310; 0.3953] 0.3176 0.5636
## Region = Southern Africa 1 0.1847 [0.1845; 0.1849] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 588726.65 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.6123 [0.6119; 0.6128] 0.7 1.5 Northern Africa
## ARGENTINA 0.8307 [0.8302; 0.8311] 1.0 1.5 Central and South America and the Caribbean
## AUSTRALIA 3.1224 [3.1212; 3.1236] 2.1 1.5 Australia and New Zealand
## AUSTRIA 2.1375 [2.1358; 2.1391] 0.5 1.5 Western Europe
## BELARUS 0.0123 [0.0122; 0.0124] 0.0 1.5 Eastern Europe
## BELGIUM 2.0611 [2.0597; 2.0625] 0.7 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0126 [0.0124; 0.0128] 0.0 1.5 Southern Europe
## BRAZIL 0.1486 [0.1485; 0.1487] 0.9 1.5 Central and South America and the Caribbean
## BULGARIA 0.0809 [0.0806; 0.0813] 0.0 1.5 Eastern Europe
## CANADA 3.7305 [3.7294; 3.7315] 3.9 1.5 Northern America
## CHILE 0.4682 [0.4676; 0.4687] 0.2 1.5 Central and South America and the Caribbean
## CHINA 0.0024 [0.0024; 0.0024] 0.1 1.5 Eastern Asia
## COLOMBIA 0.0892 [0.0891; 0.0893] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.3720 [0.3710; 0.3730] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.3551 [1.3539; 1.3562] 0.4 1.5 Eastern Europe
## ECUADOR 0.3316 [0.3311; 0.3320] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 0.4690 [0.4688; 0.4693] 1.2 1.5 Northern Africa
## ESTONIA 0.3397 [0.3381; 0.3414] 0.0 1.5 Northern Europe
## FINLAND 5.3754 [5.3721; 5.3786] 0.9 1.5 Northern Europe
## FRANCE 3.6856 [3.6849; 3.6864] 6.9 1.5 Western Europe
## GERMANY 2.8213 [2.8207; 2.8219] 6.7 1.5 Western Europe
## GREECE 2.3245 [2.3230; 2.3260] 0.7 1.5 Southern Europe
## HUNGARY 1.0685 [1.0674; 1.0696] 0.3 1.5 Eastern Europe
## INDIA 0.2404 [0.2404; 0.2405] 9.0 1.5 Southern Asia
## IRELAND 6.0138 [6.0100; 6.0175] 0.8 1.5 Northern Europe
## ITALY 1.7605 [1.7600; 1.7611] 3.1 1.5 Southern Europe
## JAPAN 2.1922 [2.1917; 2.1926] 8.3 1.5 Eastern Asia
## JORDAN 0.3112 [0.3106; 0.3118] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0286 [0.0284; 0.0287] 0.0 1.5 Central Asia
## KUWAIT 0.6898 [0.6884; 0.6913] 0.1 1.5 Western Asia
## LATVIA 0.0938 [0.0931; 0.0945] 0.0 1.5 Northern Europe
## LEBANON 0.5122 [0.5112; 0.5131] 0.1 1.5 Western Asia
## LITHUANIA 0.7074 [0.7058; 0.7089] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.3751 [3.3670; 3.3832] 0.1 1.5 Western Europe
## MEXICO 0.1822 [0.1821; 0.1823] 0.6 1.5 Central and South America and the Caribbean
## MOROCCO 0.0512 [0.0510; 0.0513] 0.1 1.5 Northern Africa
## NETHERLANDS 2.4227 [2.4214; 2.4239] 1.2 1.5 Western Europe
## NEW ZEALAND 0.0756 [0.0752; 0.0760] 0.0 1.5 Australia and New Zealand
## NORWAY 2.7219 [2.7196; 2.7243] 0.4 1.5 Northern Europe
## PAKISTAN 0.1842 [0.1841; 0.1843] 1.0 1.5 Southern Asia
## PERU 0.0920 [0.0918; 0.0922] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0583 [0.0583; 0.0584] 0.2 1.5 South-eastern Asia
## POLAND 0.0159 [0.0159; 0.0160] 0.0 1.5 Eastern Europe
## PORTUGAL 3.1361 [3.1343; 3.1379] 1.0 1.5 Southern Europe
## PUERTO RICO 1.2648 [1.2629; 1.2668] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.1968 [0.1965; 0.1972] 0.1 1.5 Eastern Europe
## RUSSIA 0.6995 [0.6993; 0.6997] 3.0 1.5 Eastern Europe
## SAUDI ARABIA 0.9389 [0.9383; 0.9395] 0.8 1.5 Western Asia
## SERBIA 0.1583 [0.1578; 0.1587] 0.0 1.5 Southern Europe
## SLOVAKIA 2.2623 [2.2602; 2.2644] 0.4 1.5 Eastern Europe
## SLOVENIA 2.2580 [2.2546; 2.2614] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.1957 [0.1955; 0.1959] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.1367 [1.1362; 1.1372] 1.7 1.5 Eastern Asia
## SPAIN 4.2439 [4.2429; 4.2448] 5.8 1.5 Southern Europe
## SWEDEN 3.2839 [3.2820; 3.2858] 0.9 1.5 Northern Europe
## SWITZERLAND 2.6708 [2.6689; 2.6726] 0.6 1.5 Western Europe
## TAIWAN 0.0939 [0.0937; 0.0941] 0.1 1.5 Eastern Asia
## THAILAND 0.1231 [0.1230; 0.1233] 0.2 1.5 South-eastern Asia
## TUNISIA 0.3557 [0.3552; 0.3563] 0.1 1.5 Northern Africa
## TÜRKIYE 1.0282 [1.0279; 1.0286] 2.3 1.5 Western Asia
## UNITED ARAB EMIRATES 0.4835 [0.4828; 0.4843] 0.1 1.5 Western Asia
## UNITED KINGDOM 4.2010 [4.2002; 4.2019] 8.0 1.5 Northern Europe
## UNITED STATES 2.2311 [2.2308; 2.2313] 20.7 1.5 Northern America
## URUGUAY 0.7672 [0.7657; 0.7688] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.6107 [0.6102; 0.6111] 0.5 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.7083 [1.7082; 1.7084] 18889.06 0
## Random effects model 0.5137 [0.3997; 0.6602] -5.20 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0655 [1.0709; 2.8146]; tau = 1.0323 [1.0349; 1.6777]
## I^2 = 100.0%; H = 4358.34
##
## Test of heterogeneity:
## Q d.f. p-value
## 1215688307.80 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.4188 [2.4186; 2.4191] 10711405.87 100.0%
## Region = Central and South America and t ... 10 0.3532 [0.3531; 0.3533] 31126765.26 100.0%
## Region = Northern Europe 8 4.1760 [4.1753; 4.1767] 8114577.22 100.0%
## Region = Eastern Europe 8 0.8002 [0.7999; 0.8004] 15403456.19 100.0%
## Region = Southern Europe 8 2.9893 [2.9888; 2.9898] 27978586.73 100.0%
## Region = Western Europe 7 3.0454 [3.0449; 3.0458] 6660558.83 100.0%
## Region = Australia and New Zealand 2 3.0685 [3.0673; 3.0696] 1719042.83 100.0%
## Region = Eastern Asia 4 1.8043 [1.8040; 1.8046] 68292960.79 100.0%
## Region = Central Asia 1 0.0286 [0.0284; 0.0287] 0.00 --
## Region = South-eastern Asia 2 0.0908 [0.0908; 0.0909] 696449.66 100.0%
## Region = Southern Asia 2 0.2339 [0.2339; 0.2340] 818872.74 100.0%
## Region = Western Asia 6 0.9282 [0.9279; 0.9284] 2616731.73 100.0%
## Region = Northern Africa 4 0.4780 [0.4778; 0.4781] 3799056.18 100.0%
## Region = Southern Africa 1 0.1957 [0.1955; 0.1959] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1037749843.75 13 0
## Within groups 177938464.05 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.8849 [1.7432; 4.7745] 0.1321 0.3635
## Region = Central and South America and t ... 10 0.3334 [0.1927; 0.5769] 0.7825 0.8846
## Region = Northern Europe 8 1.5123 [1.1803; 1.9378] 0.1279 0.3577
## Region = Eastern Europe 8 0.2275 [0.1323; 0.3912] 0.6122 0.7824
## Region = Southern Europe 8 0.7412 [0.4963; 1.1069] 0.3350 0.5788
## Region = Western Europe 7 2.6828 [2.2788; 3.1586] 0.0486 0.2204
## Region = Australia and New Zealand 2 0.4858 [0.0127; 18.6292] 6.9238 2.6313
## Region = Eastern Asia 4 0.1533 [0.0413; 0.5690] 1.7904 1.3381
## Region = Central Asia 1 0.0286 [0.0284; 0.0287] -- --
## Region = South-eastern Asia 2 0.0848 [0.0408; 0.1762] 0.2789 0.5281
## Region = Southern Asia 2 0.2104 [0.1621; 0.2732] 0.0355 0.1883
## Region = Western Asia 6 0.6096 [0.4629; 0.8028] 0.1184 0.3441
## Region = Northern Africa 4 0.2689 [0.1611; 0.4489] 0.2735 0.5230
## Region = Southern Africa 1 0.1957 [0.1955; 0.1959] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 630304.31 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.8746 [0.8741; 0.8750] 0.9 1.5 Northern Africa
## ARGENTINA 0.9517 [0.9512; 0.9522] 1.0 1.5 Central and South America and the Caribbean
## AUSTRALIA 5.4618 [5.4602; 5.4633] 3.3 1.5 Australia and New Zealand
## AUSTRIA 2.3277 [2.3261; 2.3294] 0.5 1.5 Western Europe
## BELARUS 0.0146 [0.0145; 0.0147] 0.0 1.5 Eastern Europe
## BELGIUM 2.0963 [2.0949; 2.0978] 0.6 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0180 [0.0177; 0.0182] 0.0 1.5 Southern Europe
## BRAZIL 0.1909 [0.1908; 0.1910] 1.0 1.5 Central and South America and the Caribbean
## BULGARIA 0.1039 [0.1035; 0.1043] 0.0 1.5 Eastern Europe
## CANADA 4.2330 [4.2318; 4.2341] 3.8 1.5 Northern America
## CHILE 0.5657 [0.5651; 0.5663] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0026 [0.0026; 0.0026] 0.1 1.5 Eastern Asia
## COLOMBIA 0.1081 [0.1080; 0.1083] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.4368 [0.4358; 0.4379] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.5417 [1.5405; 1.5430] 0.4 1.5 Eastern Europe
## ECUADOR 0.3778 [0.3774; 0.3783] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 0.6020 [0.6018; 0.6023] 1.4 1.5 Northern Africa
## ESTONIA 0.4430 [0.4412; 0.4449] 0.0 1.5 Northern Europe
## FINLAND 4.9036 [4.9005; 4.9067] 0.7 1.5 Northern Europe
## FRANCE 4.0148 [4.0140; 4.0156] 6.6 1.5 Western Europe
## GERMANY 3.0256 [3.0250; 3.0263] 6.3 1.5 Western Europe
## GREECE 2.3194 [2.3179; 2.3209] 0.6 1.5 Southern Europe
## HUNGARY 1.1791 [1.1780; 1.1803] 0.3 1.5 Eastern Europe
## INDIA 0.2673 [0.2672; 0.2673] 8.8 1.5 Southern Asia
## IRELAND 6.5956 [6.5917; 6.5995] 0.8 1.5 Northern Europe
## ITALY 1.9060 [1.9054; 1.9065] 2.9 1.5 Southern Europe
## JAPAN 2.7970 [2.7965; 2.7974] 9.1 1.5 Eastern Asia
## JORDAN 0.4901 [0.4893; 0.4909] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0399 [0.0398; 0.0401] 0.0 1.5 Central Asia
## KUWAIT 0.5753 [0.5740; 0.5766] 0.1 1.5 Western Asia
## LATVIA 0.1005 [0.0998; 0.1013] 0.0 1.5 Northern Europe
## LEBANON 0.5807 [0.5797; 0.5817] 0.1 1.5 Western Asia
## LITHUANIA 0.8691 [0.8674; 0.8709] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.5380 [3.5298; 3.5462] 0.0 1.5 Western Europe
## MEXICO 0.2060 [0.2059; 0.2062] 0.6 1.5 Central and South America and the Caribbean
## MOROCCO 0.0548 [0.0547; 0.0550] 0.0 1.5 Northern Africa
## NETHERLANDS 2.6728 [2.6715; 2.6741] 1.2 1.5 Western Europe
## NEW ZEALAND 0.0869 [0.0864; 0.0873] 0.0 1.5 Australia and New Zealand
## NORWAY 2.8620 [2.8596; 2.8645] 0.4 1.5 Northern Europe
## PAKISTAN 0.2062 [0.2061; 0.2063] 1.0 1.5 Southern Asia
## PERU 0.1047 [0.1045; 0.1049] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0678 [0.0678; 0.0679] 0.2 1.5 South-eastern Asia
## POLAND 0.0388 [0.0387; 0.0389] 0.0 1.5 Eastern Europe
## PORTUGAL 3.6144 [3.6125; 3.6163] 1.0 1.5 Southern Europe
## PUERTO RICO 1.2472 [1.2452; 1.2491] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.1747 [0.1744; 0.1750] 0.1 1.5 Eastern Europe
## RUSSIA 0.9011 [0.9009; 0.9014] 3.3 1.5 Eastern Europe
## SAUDI ARABIA 1.2680 [1.2673; 1.2687] 1.0 1.5 Western Asia
## SERBIA 0.3061 [0.3055; 0.3067] 0.1 1.5 Southern Europe
## SLOVAKIA 2.3157 [2.3136; 2.3179] 0.3 1.5 Eastern Europe
## SLOVENIA 2.6181 [2.6145; 2.6218] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2145 [0.2143; 0.2147] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.2171 [1.2166; 1.2176] 1.6 1.5 Eastern Asia
## SPAIN 4.4807 [4.4797; 4.4817] 5.3 1.5 Southern Europe
## SWEDEN 3.2563 [3.2544; 3.2582] 0.8 1.5 Northern Europe
## SWITZERLAND 2.9188 [2.9169; 2.9207] 0.6 1.5 Western Europe
## TAIWAN 0.1689 [0.1687; 0.1692] 0.1 1.5 Eastern Asia
## THAILAND 0.1475 [0.1474; 0.1477] 0.3 1.5 South-eastern Asia
## TUNISIA 0.3975 [0.3969; 0.3982] 0.1 1.5 Northern Africa
## TÜRKIYE 1.5192 [1.5187; 1.5196] 3.0 1.5 Western Asia
## UNITED ARAB EMIRATES 0.7207 [0.7198; 0.7216] 0.2 1.5 Western Asia
## UNITED KINGDOM 5.0509 [5.0500; 5.0518] 8.4 1.5 Northern Europe
## UNITED STATES 2.3340 [2.3338; 2.3343] 19.0 1.5 Northern America
## URUGUAY 0.9316 [0.9299; 0.9333] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.7561 [0.7556; 0.7566] 0.6 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.9313 [1.9312; 1.9314] 24919.17 0
## Random effects model 0.6103 [0.4755; 0.7832] -3.88 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0531 [1.0412; 2.7206]; tau = 1.0262 [1.0204; 1.6494]
## I^2 = 100.0%; H = 4665.04
##
## Test of heterogeneity:
## Q d.f. p-value
## 1392808286.69 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.5807 [2.5804; 2.5809] 16243650.56 100.0%
## Region = Central and South America and t ... 10 0.4113 [0.4112; 0.4114] 34727583.30 100.0%
## Region = Northern Europe 8 4.8073 [4.8065; 4.8080] 9689188.04 100.0%
## Region = Eastern Europe 8 0.9599 [0.9596; 0.9601] 16775840.94 100.0%
## Region = Southern Europe 8 3.1679 [3.1674; 3.1684] 29961138.78 100.0%
## Region = Western Europe 7 3.2955 [3.2950; 3.2959] 7825704.39 100.0%
## Region = Australia and New Zealand 2 5.3928 [5.3912; 5.3943] 2477763.94 100.0%
## Region = Eastern Asia 4 2.2796 [2.2793; 2.2800] 84529640.98 100.0%
## Region = Central Asia 1 0.0399 [0.0398; 0.0401] 0.00 --
## Region = South-eastern Asia 2 0.1079 [0.1078; 0.1080] 897197.16 100.0%
## Region = Southern Asia 2 0.2601 [0.2601; 0.2602] 887508.04 100.0%
## Region = Western Asia 6 1.3339 [1.3335; 1.3342] 4748278.61 100.0%
## Region = Northern Africa 4 0.6441 [0.6439; 0.6443] 5783925.88 100.0%
## Region = Southern Africa 1 0.2145 [0.2143; 0.2147] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1178260866.08 13 0
## Within groups 214547420.61 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.1432 [1.7539; 5.6329] 0.1772 0.4209
## Region = Central and South America and t ... 10 0.3891 [0.2292; 0.6606] 0.7295 0.8541
## Region = Northern Europe 8 1.6646 [1.2769; 2.1701] 0.1464 0.3826
## Region = Eastern Europe 8 0.2811 [0.1648; 0.4796] 0.5944 0.7710
## Region = Southern Europe 8 0.9052 [0.6077; 1.3483] 0.3306 0.5750
## Region = Western Europe 7 2.8782 [2.4286; 3.4110] 0.0526 0.2293
## Region = Australia and New Zealand 2 0.6889 [0.0119; 39.8600] 8.5739 2.9281
## Region = Eastern Asia 4 0.1959 [0.0497; 0.7720] 1.9583 1.3994
## Region = Central Asia 1 0.0399 [0.0398; 0.0401] -- --
## Region = South-eastern Asia 2 0.1000 [0.0467; 0.2142] 0.3017 0.5493
## Region = Southern Asia 2 0.2347 [0.1820; 0.3027] 0.0337 0.1835
## Region = Western Asia 6 0.7812 [0.5708; 1.0692] 0.1538 0.3922
## Region = Northern Africa 4 0.3273 [0.1896; 0.5651] 0.3105 0.5572
## Region = Southern Africa 1 0.2145 [0.2143; 0.2147] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 675005.23 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 1.0163 [1.0158; 1.0168] 0.9 1.5 Northern Africa
## ARGENTINA 1.1057 [1.1052; 1.1062] 1.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 6.8676 [6.8659; 6.8693] 3.8 1.5 Australia and New Zealand
## AUSTRIA 2.4571 [2.4554; 2.4588] 0.5 1.5 Western Europe
## BELARUS 0.0133 [0.0132; 0.0134] 0.0 1.5 Eastern Europe
## BELGIUM 2.3686 [2.3671; 2.3700] 0.6 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0204 [0.0202; 0.0207] 0.0 1.5 Southern Europe
## BRAZIL 0.2552 [0.2551; 0.2553] 1.2 1.5 Central and South America and the Caribbean
## BULGARIA 0.1449 [0.1444; 0.1453] 0.0 1.5 Eastern Europe
## CANADA 4.8298 [4.8286; 4.8310] 4.0 1.5 Northern America
## CHILE 0.6778 [0.6772; 0.6785] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0034 [0.0034; 0.0034] 0.1 1.5 Eastern Asia
## COLOMBIA 0.1500 [0.1498; 0.1502] 0.2 1.5 Central and South America and the Caribbean
## CROATIA 0.5667 [0.5655; 0.5679] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 2.0048 [2.0034; 2.0062] 0.5 1.5 Eastern Europe
## ECUADOR 0.4372 [0.4367; 0.4377] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 0.8155 [0.8152; 0.8158] 1.7 1.5 Northern Africa
## ESTONIA 0.6044 [0.6022; 0.6066] 0.0 1.5 Northern Europe
## FINLAND 4.7511 [4.7480; 4.7541] 0.6 1.5 Northern Europe
## FRANCE 4.2238 [4.2230; 4.2247] 6.3 1.5 Western Europe
## GERMANY 3.2835 [3.2828; 3.2841] 6.2 1.5 Western Europe
## GREECE 2.3751 [2.3736; 2.3766] 0.6 1.5 Southern Europe
## HUNGARY 1.1061 [1.1050; 1.1072] 0.2 1.5 Eastern Europe
## INDIA 0.3002 [0.3002; 0.3003] 9.1 1.5 Southern Asia
## IRELAND 8.2475 [8.2432; 8.2519] 0.9 1.5 Northern Europe
## ITALY 1.9980 [1.9974; 1.9986] 2.8 1.5 Southern Europe
## JAPAN 3.0553 [3.0548; 3.0558] 9.0 1.5 Eastern Asia
## JORDAN 0.5068 [0.5060; 0.5075] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0641 [0.0639; 0.0643] 0.0 1.5 Central Asia
## KUWAIT 0.6329 [0.6316; 0.6342] 0.1 1.5 Western Asia
## LATVIA 0.0874 [0.0867; 0.0881] 0.0 1.5 Northern Europe
## LEBANON 0.6932 [0.6922; 0.6943] 0.1 1.5 Western Asia
## LITHUANIA 0.9057 [0.9039; 0.9075] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.4906 [3.4825; 3.4986] 0.0 1.5 Western Europe
## MEXICO 0.2250 [0.2249; 0.2252] 0.6 1.5 Central and South America and the Caribbean
## MOROCCO 0.0612 [0.0611; 0.0613] 0.0 1.5 Northern Africa
## NETHERLANDS 2.8345 [2.8332; 2.8359] 1.1 1.5 Western Europe
## NEW ZEALAND 0.1001 [0.0997; 0.1006] 0.0 1.5 Australia and New Zealand
## NORWAY 3.0177 [3.0153; 3.0202] 0.4 1.5 Northern Europe
## PAKISTAN 0.2419 [0.2418; 0.2420] 1.1 1.5 Southern Asia
## PERU 0.1435 [0.1433; 0.1437] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0781 [0.0780; 0.0782] 0.2 1.5 South-eastern Asia
## POLAND 0.0669 [0.0667; 0.0670] 0.1 1.5 Eastern Europe
## PORTUGAL 3.7551 [3.7532; 3.7571] 0.9 1.5 Southern Europe
## PUERTO RICO 1.1861 [1.1842; 1.1881] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.1684 [0.1681; 0.1687] 0.1 1.5 Eastern Europe
## RUSSIA 0.7960 [0.7958; 0.7963] 2.7 1.5 Eastern Europe
## SAUDI ARABIA 1.0275 [1.0269; 1.0281] 0.8 1.5 Western Asia
## SERBIA 0.6683 [0.6674; 0.6692] 0.1 1.5 Southern Europe
## SLOVAKIA 2.5507 [2.5485; 2.5529] 0.3 1.5 Eastern Europe
## SLOVENIA 2.9286 [2.9248; 2.9325] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2358 [0.2356; 0.2360] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.3430 [1.3425; 1.3435] 1.6 1.5 Eastern Asia
## SPAIN 4.4954 [4.4944; 4.4964] 4.8 1.5 Southern Europe
## SWEDEN 3.2796 [3.2777; 3.2815] 0.7 1.5 Northern Europe
## SWITZERLAND 3.2163 [3.2143; 3.2183] 0.6 1.5 Western Europe
## TAIWAN 0.2086 [0.2083; 0.2089] 0.1 1.5 Eastern Asia
## THAILAND 0.1601 [0.1599; 0.1602] 0.3 1.5 South-eastern Asia
## TUNISIA 0.4702 [0.4696; 0.4709] 0.1 1.5 Northern Africa
## TÜRKIYE 2.0758 [2.0753; 2.0764] 3.8 1.5 Western Asia
## UNITED ARAB EMIRATES 0.9940 [0.9929; 0.9951] 0.2 1.5 Western Asia
## UNITED KINGDOM 5.8870 [5.8860; 5.8879] 8.9 1.5 Northern Europe
## UNITED STATES 2.4324 [2.4321; 2.4326] 18.0 1.5 Northern America
## URUGUAY 1.1259 [1.1241; 1.1278] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.7361 [0.7356; 0.7366] 0.5 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.0947 [2.0946; 2.0948] 29431.77 0
## Random effects model 0.6982 [0.5421; 0.8993] -2.78 0.0054
##
## Quantifying heterogeneity:
## tau^2 = 1.0833 [1.0286; 2.6697]; tau = 1.0408 [1.0142; 1.6339]
## I^2 = 100.0%; H = 4981.73
##
## Test of heterogeneity:
## Q d.f. p-value
## 1588329792.44 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.7564 [2.7561; 2.7566] 24452429.19 100.0%
## Region = Central and South America and t ... 10 0.4609 [0.4607; 0.4610] 35451580.52 100.0%
## Region = Northern Europe 8 5.5532 [5.5524; 5.5540] 13999343.22 100.0%
## Region = Eastern Europe 8 0.9263 [0.9261; 0.9266] 23034030.61 100.0%
## Region = Southern Europe 8 3.1895 [3.1890; 3.1900] 28456519.38 100.0%
## Region = Western Europe 7 3.5193 [3.5188; 3.5197] 7219721.84 100.0%
## Region = Australia and New Zealand 2 6.7867 [6.7850; 6.7884] 3008527.49 100.0%
## Region = Eastern Asia 4 2.4581 [2.4577; 2.4585] 102850269.35 100.0%
## Region = Central Asia 1 0.0641 [0.0639; 0.0643] 0.00 --
## Region = South-eastern Asia 2 0.1184 [0.1183; 0.1185] 868951.56 100.0%
## Region = Southern Asia 2 0.2932 [0.2932; 0.2933] 732713.55 100.0%
## Region = Western Asia 6 1.6927 [1.6923; 1.6931] 11069361.49 100.0%
## Region = Northern Africa 4 0.8188 [0.8186; 0.8191] 6492242.32 100.0%
## Region = Southern Africa 1 0.2358 [0.2356; 0.2360] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1330694101.94 13 0
## Within groups 257635690.51 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.4275 [1.7500; 6.7130] 0.2353 0.4850
## Region = Central and South America and t ... 10 0.4571 [0.2791; 0.7486] 0.6334 0.7958
## Region = Northern Europe 8 1.7980 [1.3256; 2.4388] 0.1935 0.4399
## Region = Eastern Europe 8 0.3154 [0.1734; 0.5735] 0.7446 0.8629
## Region = Southern Europe 8 1.0774 [0.7377; 1.5734] 0.2987 0.5465
## Region = Western Europe 7 3.0697 [2.6234; 3.5919] 0.0450 0.2121
## Region = Australia and New Zealand 2 0.8293 [0.0132; 52.2592] 8.9383 2.9897
## Region = Eastern Asia 4 0.2326 [0.0559; 0.9683] 2.1177 1.4552
## Region = Central Asia 1 0.0641 [0.0639; 0.0643] -- --
## Region = South-eastern Asia 2 0.1118 [0.0554; 0.2259] 0.2573 0.5072
## Region = Southern Asia 2 0.2695 [0.2181; 0.3331] 0.0233 0.1528
## Region = Western Asia 6 0.8822 [0.5519; 1.4102] 0.3437 0.5863
## Region = Northern Africa 4 0.3930 [0.2340; 0.6600] 0.2800 0.5291
## Region = Southern Africa 1 0.2358 [0.2356; 0.2360] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 644108.45 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 1.3989 [1.3983; 1.3995] 1.2 1.5 Northern Africa
## ARGENTINA 1.1449 [1.1443; 1.1454] 1.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 8.0026 [8.0007; 8.0044] 4.1 1.5 Australia and New Zealand
## AUSTRIA 3.4469 [3.4449; 3.4489] 0.6 1.5 Western Europe
## BELARUS 0.0210 [0.0209; 0.0212] 0.0 1.5 Eastern Europe
## BELGIUM 3.6912 [3.6894; 3.6931] 0.9 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0389 [0.0386; 0.0393] 0.0 1.5 Southern Europe
## BRAZIL 0.3278 [0.3277; 0.3280] 1.4 1.5 Central and South America and the Caribbean
## BULGARIA 0.4235 [0.4227; 0.4243] 0.1 1.5 Eastern Europe
## CANADA 5.4479 [5.4466; 5.4491] 4.2 1.5 Northern America
## CHILE 0.7712 [0.7705; 0.7719] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0048 [0.0048; 0.0048] 0.1 1.5 Eastern Asia
## COLOMBIA 0.1520 [0.1518; 0.1521] 0.2 1.5 Central and South America and the Caribbean
## CROATIA 0.9388 [0.9372; 0.9403] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 2.9635 [2.9618; 2.9652] 0.7 1.5 Eastern Europe
## ECUADOR 0.4496 [0.4491; 0.4501] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 1.1842 [1.1839; 1.1846] 2.4 1.5 Northern Africa
## ESTONIA 0.8538 [0.8512; 0.8564] 0.0 1.5 Northern Europe
## FINLAND 4.9233 [4.9203; 4.9264] 0.6 1.5 Northern Europe
## FRANCE 4.3282 [4.3274; 4.3290] 5.9 1.5 Western Europe
## GERMANY 3.5834 [3.5827; 3.5840] 6.2 1.5 Western Europe
## GREECE 2.9066 [2.9049; 2.9083] 0.7 1.5 Southern Europe
## HUNGARY 1.1861 [1.1849; 1.1872] 0.2 1.5 Eastern Europe
## INDIA 0.3294 [0.3293; 0.3294] 9.2 1.5 Southern Asia
## IRELAND 8.8947 [8.8902; 8.8992] 0.9 1.5 Northern Europe
## ITALY 2.0556 [2.0550; 2.0562] 2.6 1.5 Southern Europe
## JAPAN 3.3683 [3.3678; 3.3688] 9.1 1.5 Eastern Asia
## JORDAN 0.8144 [0.8134; 0.8153] 0.2 1.5 Western Asia
## KAZAKHSTAN 0.0473 [0.0471; 0.0474] 0.0 1.5 Central Asia
## KUWAIT 0.8609 [0.8594; 0.8624] 0.1 1.5 Western Asia
## LATVIA 0.1269 [0.1260; 0.1277] 0.0 1.5 Northern Europe
## LEBANON 0.8859 [0.8847; 0.8870] 0.1 1.5 Western Asia
## LITHUANIA 1.2196 [1.2174; 1.2217] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.6592 [3.6511; 3.6674] 0.0 1.5 Western Europe
## MEXICO 0.2381 [0.2379; 0.2382] 0.6 1.5 Central and South America and the Caribbean
## MOROCCO 0.0617 [0.0615; 0.0618] 0.0 1.5 Northern Africa
## NETHERLANDS 3.0032 [3.0018; 3.0045] 1.1 1.5 Western Europe
## NEW ZEALAND 0.1140 [0.1135; 0.1145] 0.0 1.5 Australia and New Zealand
## NORWAY 3.1358 [3.1333; 3.1384] 0.3 1.5 Northern Europe
## PAKISTAN 0.2771 [0.2770; 0.2773] 1.2 1.5 Southern Asia
## PERU 0.1565 [0.1563; 0.1567] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0931 [0.0930; 0.0932] 0.2 1.5 South-eastern Asia
## POLAND 0.3348 [0.3345; 0.3352] 0.3 1.5 Eastern Europe
## PORTUGAL 3.5745 [3.5725; 3.5764] 0.8 1.5 Southern Europe
## PUERTO RICO 1.1569 [1.1550; 1.1589] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.1887 [0.1883; 0.1890] 0.1 1.5 Eastern Europe
## RUSSIA 0.1847 [0.1846; 0.1849] 0.6 1.5 Eastern Europe
## SAUDI ARABIA 0.8962 [0.8957; 0.8968] 0.6 1.5 Western Asia
## SERBIA 1.0159 [1.0148; 1.0170] 0.2 1.5 Southern Europe
## SLOVAKIA 3.5712 [3.5686; 3.5739] 0.4 1.5 Eastern Europe
## SLOVENIA 3.3094 [3.3053; 3.3135] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2596 [0.2594; 0.2599] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.5523 [1.5517; 1.5529] 1.7 1.5 Eastern Asia
## SPAIN 4.7605 [4.7595; 4.7615] 4.7 1.5 Southern Europe
## SWEDEN 3.2710 [3.2692; 3.2729] 0.7 1.5 Northern Europe
## SWITZERLAND 3.4599 [3.4578; 3.4619] 0.6 1.5 Western Europe
## TAIWAN 0.2559 [0.2555; 0.2562] 0.1 1.5 Eastern Asia
## THAILAND 0.1710 [0.1709; 0.1712] 0.2 1.5 South-eastern Asia
## TUNISIA 0.5303 [0.5296; 0.5310] 0.1 1.5 Northern Africa
## TÜRKIYE 2.5282 [2.5276; 2.5288] 4.3 1.5 Western Asia
## UNITED ARAB EMIRATES 0.7037 [0.7028; 0.7046] 0.1 1.5 Western Asia
## UNITED KINGDOM 6.7927 [6.7917; 6.7938] 9.5 1.5 Northern Europe
## UNITED STATES 2.4771 [2.4768; 2.4773] 16.9 1.5 Northern America
## URUGUAY 1.1942 [1.1923; 1.1961] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.5555 [0.5550; 0.5559] 0.4 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.3081 [2.3080; 2.3082] 34751.09 0
## Random effects model 0.8178 [0.6314; 1.0592] -1.52 0.1275
##
## Quantifying heterogeneity:
## tau^2 = 1.1323 [1.0552; 2.7138]; tau = 1.0641 [1.0272; 1.6473]
## I^2 = 100.0%; H = 5322.52
##
## Test of heterogeneity:
## Q d.f. p-value
## 1813068895.36 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.8966 [2.8963; 2.8969] 36042624.04 100.0%
## Region = Central and South America and t ... 10 0.4771 [0.4770; 0.4772] 31972062.34 100.0%
## Region = Northern Europe 8 6.3006 [6.2998; 6.3015] 17829890.73 100.0%
## Region = Eastern Europe 8 0.9297 [0.9295; 0.9300] 64190322.55 100.0%
## Region = Southern Europe 8 3.3399 [3.3394; 3.3404] 29127522.72 100.0%
## Region = Western Europe 7 3.8010 [3.8005; 3.8014] 3345905.84 100.0%
## Region = Australia and New Zealand 2 7.9103 [7.9085; 7.9121] 3496443.04 100.0%
## Region = Eastern Asia 4 2.6703 [2.6699; 2.6707] 128046083.35 100.0%
## Region = Central Asia 1 0.0473 [0.0471; 0.0474] 0.00 --
## Region = South-eastern Asia 2 0.1301 [0.1300; 0.1302] 716809.15 100.0%
## Region = Southern Asia 2 0.3229 [0.3229; 0.3230] 544010.22 100.0%
## Region = Western Asia 6 2.0187 [2.0183; 2.0191] 18127816.24 100.0%
## Region = Northern Africa 4 1.1724 [1.1721; 1.1727] 8891027.38 100.0%
## Region = Southern Africa 1 0.2596 [0.2594; 0.2599] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1470738377.77 13 0
## Within groups 342330517.60 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.6735 [1.6968; 7.9528] 0.3106 0.5573
## Region = Central and South America and t ... 10 0.4734 [0.3006; 0.7454] 0.5366 0.7325
## Region = Northern Europe 8 2.1168 [1.5180; 2.9519] 0.2303 0.4799
## Region = Eastern Europe 8 0.4359 [0.1627; 1.1677] 2.0222 1.4220
## Region = Southern Europe 8 1.3711 [0.9469; 1.9852] 0.2853 0.5342
## Region = Western Europe 7 3.5777 [3.2343; 3.9576] 0.0186 0.1362
## Region = Australia and New Zealand 2 0.9551 [0.0148; 61.5841] 9.0376 3.0063
## Region = Eastern Asia 4 0.2829 [0.0650; 1.2319] 2.2534 1.5011
## Region = Central Asia 1 0.0473 [0.0471; 0.0474] -- --
## Region = South-eastern Asia 2 0.1262 [0.0696; 0.2290] 0.1848 0.4299
## Region = Southern Asia 2 0.3021 [0.2551; 0.3578] 0.0149 0.1221
## Region = Western Asia 6 0.9984 [0.5524; 1.8042] 0.5470 0.7396
## Region = Northern Africa 4 0.4825 [0.2877; 0.8089] 0.2782 0.5274
## Region = Southern Africa 1 0.2596 [0.2594; 0.2599] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 847727.58 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 2.9210 [2.9202; 2.9219] 2.3 1.5 Northern Africa
## ARGENTINA 1.2095 [1.2090; 1.2101] 1.0 1.5 Central and South America and the Caribbean
## AUSTRALIA 8.6417 [8.6397; 8.6436] 4.0 1.5 Australia and New Zealand
## AUSTRIA 3.6534 [3.6513; 3.6555] 0.6 1.5 Western Europe
## BELARUS 0.0391 [0.0389; 0.0393] 0.0 1.5 Eastern Europe
## BELGIUM 4.3393 [4.3373; 4.3413] 0.9 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0747 [0.0742; 0.0752] 0.0 1.5 Southern Europe
## BRAZIL 0.4008 [0.4007; 0.4010] 1.6 1.5 Central and South America and the Caribbean
## BULGARIA 0.6955 [0.6945; 0.6965] 0.1 1.5 Eastern Europe
## CANADA 6.0241 [6.0228; 6.0254] 4.2 1.5 Northern America
## CHILE 0.8596 [0.8589; 0.8603] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0065 [0.0065; 0.0066] 0.2 1.5 Eastern Asia
## COLOMBIA 0.1610 [0.1608; 0.1612] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 1.1064 [1.1047; 1.1081] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 3.5544 [3.5525; 3.5563] 0.7 1.5 Eastern Europe
## ECUADOR 0.4751 [0.4746; 0.4757] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 1.9074 [1.9069; 1.9078] 3.5 1.5 Northern Africa
## ESTONIA 1.1673 [1.1643; 1.1704] 0.0 1.5 Northern Europe
## FINLAND 5.0831 [5.0800; 5.0862] 0.5 1.5 Northern Europe
## FRANCE 4.4210 [4.4201; 4.4218] 5.4 1.5 Western Europe
## GERMANY 3.7208 [3.7201; 3.7215] 5.8 1.5 Western Europe
## GREECE 3.2721 [3.2703; 3.2740] 0.7 1.5 Southern Europe
## HUNGARY 1.2721 [1.2710; 1.2733] 0.2 1.5 Eastern Europe
## INDIA 0.3586 [0.3586; 0.3587] 9.1 1.5 Southern Asia
## IRELAND 8.9694 [8.9649; 8.9739] 0.8 1.5 Northern Europe
## ITALY 2.2635 [2.2629; 2.2641] 2.6 1.5 Southern Europe
## JAPAN 3.7304 [3.7298; 3.7310] 9.0 1.5 Eastern Asia
## JORDAN 1.0108 [1.0097; 1.0118] 0.2 1.5 Western Asia
## KAZAKHSTAN 0.0662 [0.0660; 0.0664] 0.0 1.5 Central Asia
## KUWAIT 1.7104 [1.7083; 1.7125] 0.1 1.5 Western Asia
## LATVIA 0.1982 [0.1971; 0.1992] 0.0 1.5 Northern Europe
## LEBANON 0.9871 [0.9858; 0.9883] 0.1 1.5 Western Asia
## LITHUANIA 1.1829 [1.1808; 1.1850] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.8108 [3.8026; 3.8190] 0.0 1.5 Western Europe
## MEXICO 0.2819 [0.2817; 0.2820] 0.7 1.5 Central and South America and the Caribbean
## MOROCCO 0.1094 [0.1092; 0.1095] 0.1 1.5 Northern Africa
## NETHERLANDS 3.0973 [3.0959; 3.0987] 1.0 1.5 Western Europe
## NEW ZEALAND 0.1255 [0.1250; 0.1260] 0.0 1.5 Australia and New Zealand
## NORWAY 3.3743 [3.3717; 3.3769] 0.3 1.5 Northern Europe
## PAKISTAN 0.3093 [0.3092; 0.3094] 1.2 1.5 Southern Asia
## PERU 0.1504 [0.1501; 0.1506] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1064 [0.1063; 0.1065] 0.2 1.5 South-eastern Asia
## POLAND 0.7340 [0.7336; 0.7345] 0.5 1.5 Eastern Europe
## PORTUGAL 3.5756 [3.5737; 3.5776] 0.7 1.5 Southern Europe
## PUERTO RICO 1.1721 [1.1701; 1.1741] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.2316 [0.2312; 0.2319] 0.1 1.5 Eastern Europe
## RUSSIA 0.2277 [0.2276; 0.2278] 0.6 1.5 Eastern Europe
## SAUDI ARABIA 1.3068 [1.3061; 1.3074] 0.8 1.5 Western Asia
## SERBIA 1.3730 [1.3717; 1.3743] 0.2 1.5 Southern Europe
## SLOVAKIA 3.1730 [3.1705; 3.1755] 0.3 1.5 Eastern Europe
## SLOVENIA 3.4915 [3.4873; 3.4957] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2695 [0.2693; 0.2697] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.8268 [1.8262; 1.8274] 1.8 1.5 Eastern Asia
## SPAIN 4.9381 [4.9371; 4.9392] 4.4 1.5 Southern Europe
## SWEDEN 3.2632 [3.2613; 3.2650] 0.6 1.5 Northern Europe
## SWITZERLAND 3.5588 [3.5567; 3.5609] 0.6 1.5 Western Europe
## TAIWAN 0.3043 [0.3040; 0.3047] 0.1 1.5 Eastern Asia
## THAILAND 0.1857 [0.1856; 0.1859] 0.2 1.5 South-eastern Asia
## TUNISIA 0.7481 [0.7473; 0.7489] 0.2 1.5 Northern Africa
## TÜRKIYE 3.2735 [3.2728; 3.2741] 5.0 1.5 Western Asia
## UNITED ARAB EMIRATES 0.6187 [0.6179; 0.6196] 0.1 1.5 Western Asia
## UNITED KINGDOM 7.4479 [7.4468; 7.4490] 9.4 1.5 Northern Europe
## UNITED STATES 2.4957 [2.4955; 2.4960] 15.3 1.5 Northern America
## URUGUAY 1.3737 [1.3716; 1.3757] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.3986 [0.3983; 0.3990] 0.2 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.4919 [2.4918; 2.4920] 40118.40 0
## Random effects model 0.9697 [0.7523; 1.2500] -0.24 0.8124
##
## Quantifying heterogeneity:
## tau^2 = 1.0907 [1.0071; 2.5640]; tau = 1.0444 [1.0035; 1.6012]
## I^2 = 100.0%; H = 5540.91
##
## Test of heterogeneity:
## Q d.f. p-value
## 1964908255.31 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 3.0144 [3.0141; 3.0147] 49308163.85 100.0%
## Region = Central and South America and t ... 10 0.5161 [0.5160; 0.5162] 31632135.56 100.0%
## Region = Northern Europe 8 6.8291 [6.8282; 6.8300] 20401798.44 100.0%
## Region = Eastern Europe 8 1.0264 [1.0261; 1.0267] 63551486.05 100.0%
## Region = Southern Europe 8 3.5018 [3.5013; 3.5023] 27034164.31 100.0%
## Region = Western Europe 7 3.9500 [3.9496; 3.9505] 3221040.26 100.0%
## Region = Australia and New Zealand 2 8.5409 [8.5390; 8.5428] 3849634.22 100.0%
## Region = Eastern Asia 4 2.9184 [2.9180; 2.9188] 157821770.32 100.0%
## Region = Central Asia 1 0.0662 [0.0660; 0.0664] 0.00 --
## Region = South-eastern Asia 2 0.1433 [0.1432; 0.1434] 678844.43 100.0%
## Region = Southern Asia 2 0.3524 [0.3524; 0.3525] 453598.80 100.0%
## Region = Western Asia 6 2.6325 [2.6321; 2.6330] 22998472.08 100.0%
## Region = Northern Africa 4 2.1124 [2.1120; 2.1128] 21165823.45 100.0%
## Region = Southern Africa 1 0.2695 [0.2693; 0.2697] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1562791323.53 13 0
## Within groups 402116931.78 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.8774 [1.6350; 9.1955] 0.3882 0.6231
## Region = Central and South America and t ... 10 0.4941 [0.3205; 0.7618] 0.4879 0.6985
## Region = Northern Europe 8 2.3782 [1.6777; 3.3711] 0.2535 0.5035
## Region = Eastern Europe 8 0.5921 [0.2485; 1.4107] 1.5695 1.2528
## Region = Southern Europe 8 1.6380 [1.1624; 2.3083] 0.2450 0.4950
## Region = Western Europe 7 3.7764 [3.4289; 4.1591] 0.0170 0.1303
## Region = Australia and New Zealand 2 1.0414 [0.0165; 65.8828] 8.9549 2.9925
## Region = Eastern Asia 4 0.3413 [0.0760; 1.5326] 2.3487 1.5325
## Region = Central Asia 1 0.0662 [0.0660; 0.0664] -- --
## Region = South-eastern Asia 2 0.1406 [0.0814; 0.2427] 0.1554 0.3942
## Region = Southern Asia 2 0.3330 [0.2881; 0.3850] 0.0109 0.1046
## Region = Western Asia 6 1.2857 [0.7262; 2.2762] 0.5096 0.7139
## Region = Northern Africa 4 0.8217 [0.4591; 1.4705] 0.3527 0.5939
## Region = Southern Africa 1 0.2695 [0.2693; 0.2697] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 802945.78 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 2.8536 [2.8528; 2.8544] 2.1 1.5 Northern Africa
## ARGENTINA 1.2643 [1.2638; 1.2649] 1.0 1.5 Central and South America and the Caribbean
## AUSTRALIA 8.7381 [8.7362; 8.7400] 3.7 1.5 Australia and New Zealand
## AUSTRIA 3.9874 [3.9852; 3.9895] 0.6 1.5 Western Europe
## BELARUS 0.0664 [0.0661; 0.0666] 0.0 1.5 Eastern Europe
## BELGIUM 4.7493 [4.7472; 4.7514] 0.9 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1682 [0.1675; 0.1690] 0.0 1.5 Southern Europe
## BRAZIL 0.5116 [0.5115; 0.5118] 1.8 1.5 Central and South America and the Caribbean
## BULGARIA 0.9454 [0.9442; 0.9466] 0.1 1.5 Eastern Europe
## CANADA 6.0709 [6.0696; 6.0722] 3.9 1.5 Northern America
## CHILE 0.9066 [0.9059; 0.9073] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0108 [0.0108; 0.0108] 0.3 1.5 Eastern Asia
## COLOMBIA 0.1810 [0.1808; 0.1812] 0.2 1.5 Central and South America and the Caribbean
## CROATIA 1.2599 [1.2581; 1.2616] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 3.9558 [3.9538; 3.9577] 0.7 1.5 Eastern Europe
## ECUADOR 0.4963 [0.4958; 0.4969] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 3.7897 [3.7891; 3.7904] 6.4 1.5 Northern Africa
## ESTONIA 1.7170 [1.7133; 1.7207] 0.0 1.5 Northern Europe
## FINLAND 5.1854 [5.1823; 5.1886] 0.5 1.5 Northern Europe
## FRANCE 4.6663 [4.6654; 4.6671] 5.2 1.5 Western Europe
## GERMANY 3.9886 [3.9879; 3.9893] 5.7 1.5 Western Europe
## GREECE 3.6452 [3.6433; 3.6471] 0.7 1.5 Southern Europe
## HUNGARY 1.3400 [1.3388; 1.3412] 0.2 1.5 Eastern Europe
## INDIA 0.4017 [0.4017; 0.4018] 9.3 1.5 Southern Asia
## IRELAND 7.2996 [7.2956; 7.3036] 0.6 1.5 Northern Europe
## ITALY 2.3084 [2.3077; 2.3090] 2.4 1.5 Southern Europe
## JAPAN 3.9854 [3.9848; 3.9859] 8.7 1.5 Eastern Asia
## JORDAN 0.3184 [0.3178; 0.3190] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0783 [0.0781; 0.0785] 0.0 1.5 Central Asia
## KUWAIT 2.3085 [2.3061; 2.3110] 0.2 1.5 Western Asia
## LATVIA 0.2861 [0.2849; 0.2874] 0.0 1.5 Northern Europe
## LEBANON 1.0700 [1.0688; 1.0713] 0.1 1.5 Western Asia
## LITHUANIA 1.4162 [1.4139; 1.4185] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.7805 [3.7724; 3.7886] 0.0 1.5 Western Europe
## MEXICO 0.3123 [0.3121; 0.3124] 0.7 1.5 Central and South America and the Caribbean
## MOROCCO 0.1609 [0.1607; 0.1611] 0.1 1.5 Northern Africa
## NETHERLANDS 3.2409 [3.2395; 3.2423] 1.0 1.5 Western Europe
## NEW ZEALAND 1.0352 [1.0336; 1.0367] 0.1 1.5 Australia and New Zealand
## NORWAY 3.4372 [3.4346; 3.4398] 0.3 1.5 Northern Europe
## PAKISTAN 0.3665 [0.3664; 0.3667] 1.3 1.5 Southern Asia
## PERU 0.1859 [0.1856; 0.1861] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1276 [0.1275; 0.1277] 0.2 1.5 South-eastern Asia
## POLAND 1.1714 [1.1709; 1.1720] 0.8 1.5 Eastern Europe
## PORTUGAL 3.6716 [3.6697; 3.6735] 0.6 1.5 Southern Europe
## PUERTO RICO 1.1985 [1.1965; 1.2006] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.2448 [0.2444; 0.2451] 0.1 1.5 Eastern Europe
## RUSSIA 0.2948 [0.2947; 0.2950] 0.7 1.5 Eastern Europe
## SAUDI ARABIA 0.3547 [0.3544; 0.3550] 0.2 1.5 Western Asia
## SERBIA 1.8492 [1.8478; 1.8507] 0.3 1.5 Southern Europe
## SLOVAKIA 3.4508 [3.4482; 3.4534] 0.3 1.5 Eastern Europe
## SLOVENIA 3.6286 [3.6243; 3.6329] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.3076 [0.3074; 0.3079] 0.3 1.5 Southern Africa
## SOUTH KOREA 2.1687 [2.1681; 2.1694] 1.9 1.5 Eastern Asia
## SPAIN 5.1063 [5.1052; 5.1073] 4.1 1.5 Southern Europe
## SWEDEN 3.2313 [3.2294; 3.2331] 0.6 1.5 Northern Europe
## SWITZERLAND 3.7111 [3.7090; 3.7132] 0.5 1.5 Western Europe
## TAIWAN 0.3825 [0.3821; 0.3829] 0.2 1.5 Eastern Asia
## THAILAND 0.2323 [0.2321; 0.2325] 0.3 1.5 South-eastern Asia
## TUNISIA 1.9419 [1.9406; 1.9433] 0.4 1.5 Northern Africa
## TÜRKIYE 3.5808 [3.5801; 3.5815] 5.1 1.5 Western Asia
## UNITED ARAB EMIRATES 0.6074 [0.6066; 0.6082] 0.1 1.5 Western Asia
## UNITED KINGDOM 7.7862 [7.7851; 7.7873] 9.0 1.5 Northern Europe
## UNITED STATES 2.5196 [2.5193; 2.5199] 14.2 1.5 Northern America
## URUGUAY 1.2352 [1.2333; 1.2372] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.3925 [0.3921; 0.3928] 0.2 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.6286 [2.6285; 2.6287] 44545.79 0
## Random effects model 1.1191 [0.8710; 1.4379] 0.88 0.3788
##
## Quantifying heterogeneity:
## tau^2 = 1.0630 [0.9666; 2.4512]; tau = 1.0310 [0.9831; 1.5656]
## I^2 = 100.0%; H = 5745.66
##
## Test of heterogeneity:
## Q d.f. p-value
## 2112804127.48 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 3.0427 [3.0424; 3.0430] 49938160.12 100.0%
## Region = Central and South America and t ... 10 0.5719 [0.5718; 0.5721] 29354395.61 100.0%
## Region = Northern Europe 8 6.9921 [6.9912; 6.9930] 20948539.78 100.0%
## Region = Eastern Europe 8 1.1949 [1.1946; 1.1952] 67049415.72 100.0%
## Region = Southern Europe 8 3.6326 [3.6321; 3.6332] 27391320.40 100.0%
## Region = Western Europe 7 4.2061 [4.2056; 4.2065] 3429166.60 100.0%
## Region = Australia and New Zealand 2 8.3363 [8.3345; 8.3381] 7980102.50 100.0%
## Region = Eastern Asia 4 3.0115 [3.0111; 3.0119] 211671394.36 100.0%
## Region = Central Asia 1 0.0783 [0.0781; 0.0785] 0.00 --
## Region = South-eastern Asia 2 0.1766 [0.1765; 0.1767] 968366.25 100.0%
## Region = Southern Asia 2 0.3971 [0.3971; 0.3972] 209018.00 100.0%
## Region = Western Asia 6 3.0011 [3.0006; 3.0017] 37647379.11 100.0%
## Region = Northern Africa 4 3.3297 [3.3293; 3.3302] 25151466.27 100.0%
## Region = Southern Africa 1 0.3076 [0.3074; 0.3079] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1631065402.74 13 0
## Within groups 481738724.73 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.9110 [1.6520; 9.2590] 0.3867 0.6218
## Region = Central and South America and t ... 10 0.5308 [0.3577; 0.7877] 0.4056 0.6368
## Region = Northern Europe 8 2.6283 [1.8363; 3.7619] 0.2678 0.5175
## Region = Eastern Europe 8 0.7471 [0.3357; 1.6624] 1.3323 1.1542
## Region = Southern Europe 8 1.9673 [1.4062; 2.7523] 0.2348 0.4846
## Region = Western Europe 7 3.9874 [3.6214; 4.3904] 0.0169 0.1300
## Region = Australia and New Zealand 2 3.0075 [0.3718; 24.3265] 2.2751 1.5084
## Region = Eastern Asia 4 0.4348 [0.0895; 2.1125] 2.6019 1.6130
## Region = Central Asia 1 0.0783 [0.0781; 0.0785] -- --
## Region = South-eastern Asia 2 0.1722 [0.0957; 0.3098] 0.1796 0.4238
## Region = Southern Asia 2 0.3837 [0.3507; 0.4198] 0.0042 0.0648
## Region = Western Asia 6 0.9201 [0.3495; 2.4223] 1.4635 1.2098
## Region = Northern Africa 4 1.3558 [0.7896; 2.3280] 0.3044 0.5517
## Region = Southern Africa 1 0.3076 [0.3074; 0.3079] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 910859.46 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0709 [0.0708; 0.0711] 0.1 1.7 Northern Africa
## ARGENTINA 0.2110 [0.2108; 0.2113] 0.2 1.7 Central and South America and the Caribbean
## AUSTRALIA 1.2865 [1.2857; 1.2873] 0.7 1.7 Australia and New Zealand
## AUSTRIA 2.1978 [2.1961; 2.1995] 0.5 1.7 Western Europe
## BELARUS 0.0056 [0.0055; 0.0057] 0.0 1.7 Eastern Europe
## BELGIUM 1.3012 [1.3001; 1.3024] 0.4 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0522 [0.0522; 0.0523] 0.3 1.7 Central and South America and the Caribbean
## BULGARIA 0.1053 [0.1049; 0.1057] 0.0 1.7 Eastern Europe
## CANADA 4.1266 [4.1254; 4.1277] 3.6 1.7 Northern America
## CHILE 0.2113 [0.2110; 0.2117] 0.1 1.7 Central and South America and the Caribbean
## CHINA 0.0009 [0.0009; 0.0009] 0.0 1.7 Eastern Asia
## COLOMBIA 0.0876 [0.0874; 0.0877] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.3160 [0.3151; 0.3168] 0.0 1.7 Southern Europe
## CZECH REPUBLIC 1.1416 [1.1405; 1.1427] 0.3 1.7 Eastern Europe
## ECUADOR 0.1948 [0.1944; 0.1951] 0.1 1.7 Central and South America and the Caribbean
## EGYPT 0.1064 [0.1063; 0.1066] 0.2 1.7 Northern Africa
## ESTONIA 0.2020 [0.2007; 0.2032] 0.0 1.7 Northern Europe
## FINLAND 4.3558 [4.3528; 4.3587] 0.6 1.7 Northern Europe
## FRANCE 3.5466 [3.5458; 3.5473] 5.8 1.7 Western Europe
## GERMANY 2.7848 [2.7842; 2.7854] 6.0 1.7 Western Europe
## GREECE 2.2484 [2.2470; 2.2499] 0.7 1.7 Southern Europe
## HUNGARY 0.7213 [0.7204; 0.7221] 0.2 1.7 Eastern Europe
## INDIA 0.1154 [0.1154; 0.1154] 3.7 1.7 Southern Asia
## IRELAND 2.8727 [2.8700; 2.8753] 0.3 1.7 Northern Europe
## ITALY 1.4918 [1.4913; 1.4923] 2.3 1.7 Southern Europe
## JAPAN 0.1016 [0.1015; 0.1017] 0.3 1.7 Eastern Asia
## JORDAN 0.1265 [0.1261; 0.1270] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1175 [0.1168; 0.1182] 0.0 1.7 Western Asia
## LATVIA 0.4164 [0.4150; 0.4178] 0.0 1.7 Northern Europe
## LEBANON 0.4021 [0.4012; 0.4031] 0.1 1.7 Western Asia
## LITHUANIA 0.3469 [0.3459; 0.3480] 0.0 1.7 Northern Europe
## LUXEMBOURG 3.2514 [3.2430; 3.2598] 0.0 1.7 Western Europe
## MEXICO 0.2788 [0.2786; 0.2789] 0.8 1.7 Central and South America and the Caribbean
## MOROCCO 0.0236 [0.0235; 0.0237] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.9869 [0.9853; 0.9885] 0.1 1.7 Australia and New Zealand
## NORWAY 3.4299 [3.4272; 3.4327] 0.4 1.7 Northern Europe
## PAKISTAN 0.0973 [0.0972; 0.0974] 0.4 1.7 Southern Asia
## PERU 0.0497 [0.0495; 0.0498] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0530 [0.0529; 0.0531] 0.1 1.7 South-eastern Asia
## POLAND 0.1513 [0.1511; 0.1515] 0.2 1.7 Eastern Europe
## PORTUGAL 3.1225 [3.1207; 3.1243] 0.9 1.7 Southern Europe
## PUERTO RICO 5.0091 [5.0053; 5.0129] 0.5 1.7 Central and South America and the Caribbean
## ROMANIA 0.3109 [0.3105; 0.3113] 0.2 1.7 Eastern Europe
## RUSSIA 0.0289 [0.0289; 0.0290] 0.1 1.7 Eastern Europe
## SAUDI ARABIA 0.3616 [0.3612; 0.3620] 0.2 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 1.4112 [1.4096; 1.4129] 0.2 1.7 Eastern Europe
## SLOVENIA 1.5309 [1.5280; 1.5337] 0.1 1.7 Southern Europe
## SOUTH AFRICA 0.1159 [0.1158; 0.1161] 0.2 1.7 Southern Africa
## SOUTH KOREA 0.9479 [0.9475; 0.9484] 1.2 1.7 Eastern Asia
## SPAIN 3.8350 [3.8340; 3.8359] 4.7 1.7 Southern Europe
## SWEDEN 3.3977 [3.3958; 3.3997] 0.8 1.7 Northern Europe
## SWITZERLAND 1.9623 [1.9607; 1.9640] 0.4 1.7 Western Europe
## TAIWAN 0.1750 [0.1747; 0.1753] 0.1 1.7 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.1223 [0.1220; 0.1227] 0.0 1.7 Northern Africa
## TÜRKIYE 1.3869 [1.3864; 1.3873] 2.6 1.7 Western Asia
## UNITED ARAB EMIRATES 0.3101 [0.3094; 0.3108] 0.1 1.7 Western Asia
## UNITED KINGDOM 3.0456 [3.0449; 3.0463] 5.0 1.7 Northern Europe
## UNITED STATES 6.6631 [6.6626; 6.6636] 53.4 1.7 Northern America
## URUGUAY 0.2539 [0.2530; 0.2548] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.6793 [0.6788; 0.6798] 0.5 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.4362 [3.4360; 3.4364] 45911.48 0
## Random effects model 0.4167 [0.2989; 0.5810] -5.16 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.7246 [1.4813; 4.5594]; tau = 1.3132 [1.2171; 2.1353]
## I^2 = 100.0%; H = 5315.64
##
## Test of heterogeneity:
## Q d.f. p-value
## 1667108391.00 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 6.4628 [6.4623; 6.4632] 10800513.39 100.0%
## Region = Central and South America and t ... 10 0.4213 [0.4211; 0.4214] 68560818.30 100.0%
## Region = Northern Europe 8 3.1351 [3.1345; 3.1357] 5193026.38 100.0%
## Region = Eastern Europe 8 0.4678 [0.4676; 0.4681] 23190551.74 100.0%
## Region = Southern Europe 6 2.7450 [2.7445; 2.7455] 22449216.66 100.0%
## Region = Western Europe 6 2.9787 [2.9783; 2.9792] 7916037.00 100.0%
## Region = Australia and New Zealand 2 1.2418 [1.2411; 1.2425] 93922.42 100.0%
## Region = Eastern Asia 4 0.4761 [0.4759; 0.4763] 38921435.09 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0530 [0.0529; 0.0531] 0.00 --
## Region = Southern Asia 2 0.1133 [0.1133; 0.1133] 158092.70 100.0%
## Region = Western Asia 6 1.1502 [1.1499; 1.1506] 10049205.60 100.0%
## Region = Northern Africa 4 0.0916 [0.0915; 0.0917] 667802.23 100.0%
## Region = Southern Africa 1 0.1159 [0.1158; 0.1161] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1479107769.50 12 0
## Within groups 188000621.51 47 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 5.2436 [3.2788; 8.3859] 0.1148 0.3388
## Region = Central and South America and t ... 10 0.2332 [0.0903; 0.6024] 2.3447 1.5312
## Region = Northern Europe 8 1.3775 [1.1026; 1.7208] 0.1031 0.3211
## Region = Eastern Europe 8 0.1763 [0.0704; 0.4416] 1.7561 1.3252
## Region = Southern Europe 6 1.6396 [1.0552; 2.5477] 0.3034 0.5508
## Region = Western Europe 6 2.3766 [1.9115; 2.9549] 0.0741 0.2722
## Region = Australia and New Zealand 2 1.1268 [0.8690; 1.4610] 0.0351 0.1874
## Region = Eastern Asia 4 0.0630 [0.0095; 0.4177] 3.7229 1.9295
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0530 [0.0529; 0.0531] -- --
## Region = Southern Asia 2 0.1060 [0.0897; 0.1252] 0.0145 0.1205
## Region = Western Asia 6 0.3124 [0.1382; 0.7063] 1.0395 1.0196
## Region = Northern Africa 4 0.0683 [0.0411; 0.1137] 0.2698 0.5194
## Region = Southern Africa 1 0.1159 [0.1158; 0.1161] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 589755.92 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.2753 [0.2750; 0.2756] 0.2 1.7 Northern Africa
## ARGENTINA 0.2916 [0.2913; 0.2918] 0.3 1.7 Central and South America and the Caribbean
## AUSTRALIA 1.4792 [1.4784; 1.4801] 0.7 1.7 Australia and New Zealand
## AUSTRIA 2.5392 [2.5375; 2.5410] 0.5 1.7 Western Europe
## BELARUS 0.0141 [0.0139; 0.0142] 0.0 1.7 Eastern Europe
## BELGIUM 1.5782 [1.5770; 1.5795] 0.4 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0739 [0.0739; 0.0740] 0.3 1.7 Central and South America and the Caribbean
## BULGARIA 0.2261 [0.2255; 0.2267] 0.0 1.7 Eastern Europe
## CANADA 4.7887 [4.7875; 4.7899] 3.7 1.7 Northern America
## CHILE 0.2263 [0.2259; 0.2266] 0.1 1.7 Central and South America and the Caribbean
## CHINA 0.0020 [0.0020; 0.0020] 0.1 1.7 Eastern Asia
## COLOMBIA 0.0840 [0.0839; 0.0841] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.3611 [0.3602; 0.3621] 0.0 1.7 Southern Europe
## CZECH REPUBLIC 1.4811 [1.4799; 1.4823] 0.4 1.7 Eastern Europe
## ECUADOR 0.2420 [0.2416; 0.2424] 0.1 1.7 Central and South America and the Caribbean
## EGYPT 0.1499 [0.1497; 0.1500] 0.3 1.7 Northern Africa
## ESTONIA 0.2543 [0.2529; 0.2557] 0.0 1.7 Northern Europe
## FINLAND 4.9666 [4.9635; 4.9698] 0.6 1.7 Northern Europe
## FRANCE 3.8551 [3.8543; 3.8559] 5.5 1.7 Western Europe
## GERMANY 3.2166 [3.2159; 3.2172] 5.9 1.7 Western Europe
## GREECE 2.6236 [2.6221; 2.6252] 0.7 1.7 Southern Europe
## HUNGARY 0.8353 [0.8344; 0.8363] 0.2 1.7 Eastern Europe
## INDIA 0.1569 [0.1569; 0.1569] 4.4 1.7 Southern Asia
## IRELAND 3.5075 [3.5046; 3.5103] 0.4 1.7 Northern Europe
## ITALY 1.6554 [1.6548; 1.6559] 2.2 1.7 Southern Europe
## JAPAN 0.1478 [0.1477; 0.1479] 0.4 1.7 Eastern Asia
## JORDAN 0.1551 [0.1546; 0.1556] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1514 [0.1507; 0.1522] 0.0 1.7 Western Asia
## LATVIA 0.5005 [0.4989; 0.5021] 0.0 1.7 Northern Europe
## LEBANON 0.5855 [0.5844; 0.5867] 0.1 1.7 Western Asia
## LITHUANIA 0.4040 [0.4028; 0.4051] 0.0 1.7 Northern Europe
## LUXEMBOURG 3.3859 [3.3774; 3.3943] 0.0 1.7 Western Europe
## MEXICO 0.2783 [0.2782; 0.2785] 0.7 1.7 Central and South America and the Caribbean
## MOROCCO 0.0386 [0.0385; 0.0387] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 1.1928 [1.1911; 1.1945] 0.1 1.7 Australia and New Zealand
## NORWAY 3.6102 [3.6074; 3.6130] 0.4 1.7 Northern Europe
## PAKISTAN 0.1376 [0.1375; 0.1377] 0.6 1.7 Southern Asia
## PERU 0.0548 [0.0547; 0.0549] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0637 [0.0636; 0.0638] 0.1 1.7 South-eastern Asia
## POLAND 0.1745 [0.1743; 0.1747] 0.2 1.7 Eastern Europe
## PORTUGAL 3.3821 [3.3803; 3.3840] 0.8 1.7 Southern Europe
## PUERTO RICO 5.9562 [5.9520; 5.9603] 0.5 1.7 Central and South America and the Caribbean
## ROMANIA 0.5838 [0.5832; 0.5843] 0.3 1.7 Eastern Europe
## RUSSIA 0.0450 [0.0449; 0.0451] 0.1 1.7 Eastern Europe
## SAUDI ARABIA 0.4092 [0.4088; 0.4096] 0.2 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 1.8496 [1.8477; 1.8515] 0.2 1.7 Eastern Europe
## SLOVENIA 1.7773 [1.7743; 1.7803] 0.1 1.7 Southern Europe
## SOUTH AFRICA 0.1586 [0.1584; 0.1588] 0.2 1.7 Southern Africa
## SOUTH KOREA 1.1294 [1.1289; 1.1299] 1.3 1.7 Eastern Asia
## SPAIN 4.4325 [4.4315; 4.4335] 4.7 1.7 Southern Europe
## SWEDEN 3.9503 [3.9482; 3.9524] 0.8 1.7 Northern Europe
## SWITZERLAND 2.1416 [2.1399; 2.1433] 0.4 1.7 Western Europe
## TAIWAN 0.1779 [0.1776; 0.1781] 0.1 1.7 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.2006 [0.2002; 0.2011] 0.0 1.7 Northern Africa
## TÜRKIYE 1.7331 [1.7326; 1.7337] 2.8 1.7 Western Asia
## UNITED ARAB EMIRATES 0.4496 [0.4488; 0.4503] 0.1 1.7 Western Asia
## UNITED KINGDOM 3.8182 [3.8174; 3.8190] 5.5 1.7 Northern Europe
## UNITED STATES 7.4008 [7.4003; 7.4013] 51.6 1.7 Northern America
## URUGUAY 0.4441 [0.4430; 0.4453] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.7331 [0.7326; 0.7337] 0.5 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.7177 [3.7175; 3.7178] 52578.79 0
## Random effects model 0.5392 [0.3863; 0.7527] -3.63 0.0003
##
## Quantifying heterogeneity:
## tau^2 = 1.7372 [1.4181; 4.2743]; tau = 1.3180 [1.1909; 2.0674]
## I^2 = 100.0%; H = 5815.15
##
## Test of heterogeneity:
## Q d.f. p-value
## 1995142807.78 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 7.1895 [7.1890; 7.1900] 10441148.67 100.0%
## Region = Central and South America and t ... 10 0.4695 [0.4693; 0.4696] 82028126.09 100.0%
## Region = Northern Europe 8 3.8175 [3.8168; 3.8182] 5634592.31 100.0%
## Region = Eastern Europe 8 0.6058 [0.6056; 0.6061] 30395566.61 100.0%
## Region = Southern Europe 6 3.1450 [3.1444; 3.1455] 27126580.72 100.0%
## Region = Western Europe 6 3.3328 [3.3323; 3.3332] 7239094.92 100.0%
## Region = Australia and New Zealand 2 1.4359 [1.4351; 1.4367] 75178.21 100.0%
## Region = Eastern Asia 4 0.5196 [0.5194; 0.5198] 55464598.05 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0637 [0.0636; 0.0638] 0.00 --
## Region = Southern Asia 2 0.1546 [0.1546; 0.1546] 135366.59 100.0%
## Region = Western Asia 6 1.4316 [1.4312; 1.4320] 13175229.32 100.0%
## Region = Northern Africa 4 0.1817 [0.1816; 0.1818] 1865787.36 100.0%
## Region = Southern Africa 1 0.1586 [0.1584; 0.1588] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1761561538.94 12 0
## Within groups 233581268.84 47 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 5.9532 [3.8858; 9.1206] 0.0948 0.3078
## Region = Central and South America and t ... 10 0.2797 [0.1072; 0.7298] 2.3939 1.5472
## Region = Northern Europe 8 1.6257 [1.3124; 2.0137] 0.0954 0.3089
## Region = Eastern Europe 8 0.2757 [0.1130; 0.6724] 1.6557 1.2867
## Region = Southern Europe 6 1.8629 [1.1810; 2.9385] 0.3245 0.5696
## Region = Western Europe 6 2.6676 [2.1929; 3.2450] 0.0600 0.2449
## Region = Australia and New Zealand 2 1.3283 [1.0758; 1.6402] 0.0232 0.1522
## Region = Eastern Asia 4 0.0875 [0.0126; 0.6096] 3.9228 1.9806
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0637 [0.0636; 0.0638] -- --
## Region = Southern Asia 2 0.1469 [0.1292; 0.1671] 0.0086 0.0930
## Region = Western Asia 6 0.4046 [0.1776; 0.9215] 1.0585 1.0288
## Region = Northern Africa 4 0.1337 [0.0760; 0.2351] 0.3321 0.5763
## Region = Southern Africa 1 0.1586 [0.1584; 0.1588] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1033334.06 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.3584 [0.3581; 0.3587] 0.3 1.6 Northern Africa
## ARGENTINA 0.4124 [0.4120; 0.4127] 0.3 1.6 Central and South America and the Caribbean
## AUSTRALIA 1.6897 [1.6888; 1.6906] 0.7 1.6 Australia and New Zealand
## AUSTRIA 3.0211 [3.0192; 3.0231] 0.5 1.6 Western Europe
## BELARUS 0.0180 [0.0178; 0.0181] 0.0 1.6 Eastern Europe
## BELGIUM 2.2085 [2.2071; 2.2100] 0.5 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.1212 [0.1211; 0.1213] 0.5 1.6 Central and South America and the Caribbean
## BULGARIA 0.3263 [0.3256; 0.3270] 0.0 1.6 Eastern Europe
## CANADA 5.3477 [5.3464; 5.3490] 3.5 1.6 Northern America
## CHILE 0.2580 [0.2576; 0.2584] 0.1 1.6 Central and South America and the Caribbean
## CHINA 0.0031 [0.0031; 0.0031] 0.1 1.6 Eastern Asia
## COLOMBIA 0.0760 [0.0759; 0.0762] 0.1 1.6 Central and South America and the Caribbean
## CROATIA 0.4543 [0.4532; 0.4553] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.8185 [1.8172; 1.8199] 0.4 1.6 Eastern Europe
## ECUADOR 0.2917 [0.2912; 0.2921] 0.1 1.6 Central and South America and the Caribbean
## EGYPT 0.2397 [0.2396; 0.2399] 0.4 1.6 Northern Africa
## ESTONIA 0.4373 [0.4355; 0.4392] 0.0 1.6 Northern Europe
## FINLAND 5.4352 [5.4319; 5.4384] 0.6 1.6 Northern Europe
## FRANCE 4.1757 [4.1748; 4.1765] 5.1 1.6 Western Europe
## GERMANY 3.5994 [3.5987; 3.6001] 5.7 1.6 Western Europe
## GREECE 2.8252 [2.8236; 2.8269] 0.6 1.6 Southern Europe
## HUNGARY 1.0156 [1.0146; 1.0167] 0.2 1.6 Eastern Europe
## INDIA 0.1918 [0.1918; 0.1919] 4.6 1.6 Southern Asia
## IRELAND 3.9361 [3.9331; 3.9391] 0.3 1.6 Northern Europe
## ITALY 1.8485 [1.8479; 1.8491] 2.1 1.6 Southern Europe
## JAPAN 0.3150 [0.3149; 0.3152] 0.8 1.6 Eastern Asia
## JORDAN 0.1833 [0.1828; 0.1838] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1562 [0.1555; 0.1569] 0.0 1.6 Western Asia
## LATVIA 0.5646 [0.5630; 0.5663] 0.0 1.6 Northern Europe
## LEBANON 0.6460 [0.6448; 0.6471] 0.1 1.6 Western Asia
## LITHUANIA 0.5072 [0.5059; 0.5085] 0.0 1.6 Northern Europe
## LUXEMBOURG 3.6404 [3.6317; 3.6491] 0.0 1.6 Western Europe
## MEXICO 0.2904 [0.2902; 0.2906] 0.6 1.6 Central and South America and the Caribbean
## MOROCCO 0.0432 [0.0431; 0.0433] 0.0 1.6 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 1.4364 [1.4345; 1.4383] 0.1 1.6 Australia and New Zealand
## NORWAY 3.9539 [3.9510; 3.9568] 0.4 1.6 Northern Europe
## PAKISTAN 0.1573 [0.1572; 0.1574] 0.5 1.6 Southern Asia
## PERU 0.0700 [0.0698; 0.0702] 0.0 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0686 [0.0685; 0.0687] 0.1 1.6 South-eastern Asia
## POLAND 0.2009 [0.2007; 0.2012] 0.1 1.6 Eastern Europe
## PORTUGAL 3.8120 [3.8101; 3.8140] 0.8 1.6 Southern Europe
## PUERTO RICO 6.7601 [6.7556; 6.7645] 0.5 1.6 Central and South America and the Caribbean
## ROMANIA 0.5874 [0.5868; 0.5879] 0.2 1.6 Eastern Europe
## RUSSIA 0.0884 [0.0883; 0.0885] 0.2 1.6 Eastern Europe
## SAUDI ARABIA 0.4869 [0.4865; 0.4873] 0.3 1.6 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 2.3771 [2.3750; 2.3793] 0.2 1.6 Eastern Europe
## SLOVENIA 2.0489 [2.0457; 2.0522] 0.1 1.6 Southern Europe
## SOUTH AFRICA 0.1825 [0.1823; 0.1827] 0.2 1.6 Southern Africa
## SOUTH KOREA 1.2561 [1.2556; 1.2566] 1.2 1.6 Eastern Asia
## SPAIN 4.8978 [4.8967; 4.8988] 4.5 1.6 Southern Europe
## SWEDEN 4.3114 [4.3092; 4.3136] 0.8 1.6 Northern Europe
## SWITZERLAND 2.4739 [2.4721; 2.4758] 0.4 1.6 Western Europe
## TAIWAN 0.1970 [0.1967; 0.1973] 0.1 1.6 Eastern Asia
## THAILAND 0.2391 [0.2389; 0.2393] 0.3 1.6 South-eastern Asia
## TUNISIA 0.2414 [0.2409; 0.2418] 0.0 1.6 Northern Africa
## TÜRKIYE 2.1626 [2.1620; 2.1632] 3.0 1.6 Western Asia
## UNITED ARAB EMIRATES 0.7453 [0.7443; 0.7462] 0.1 1.6 Western Asia
## UNITED KINGDOM 4.7452 [4.7443; 4.7461] 5.9 1.6 Northern Europe
## UNITED STATES 8.5063 [8.5057; 8.5068] 51.1 1.6 Northern America
## URUGUAY 0.5483 [0.5470; 0.5496] 0.0 1.6 Central and South America and the Caribbean
## VENEZUELA 0.7677 [0.7672; 0.7682] 0.4 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 61
##
## rate 95%-CI z p-value
## Common effect model 4.1189 [4.1187; 4.1191] 61374.22 0
## Random effects model 0.6453 [0.4606; 0.9041] -2.55 0.0109
##
## Quantifying heterogeneity:
## tau^2 = 1.8051 [1.3849; 4.0849]; tau = 1.3435 [1.1768; 2.0211]
## I^2 = 100.0%; H = 6391.62
##
## Test of heterogeneity:
## Q d.f. p-value
## 2451167882.77 60 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 8.2536 [8.2531; 8.2541] 13435200.90 100.0%
## Region = Central and South America and t ... 10 0.5072 [0.5071; 0.5073] 91099629.98 100.0%
## Region = Northern Europe 8 4.5834 [4.5827; 4.5841] 6697879.29 100.0%
## Region = Eastern Europe 8 0.6761 [0.6758; 0.6763] 39565190.94 100.0%
## Region = Southern Europe 6 3.4819 [3.4813; 3.4824] 29854484.84 100.0%
## Region = Western Europe 6 3.6900 [3.6896; 3.6905] 5356053.78 100.0%
## Region = Australia and New Zealand 2 1.6508 [1.6499; 1.6516] 51792.47 100.0%
## Region = Eastern Asia 4 0.5602 [0.5601; 0.5604] 63566747.42 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.1672 [0.1671; 0.1673] 2618838.90 100.0%
## Region = Southern Asia 2 0.1878 [0.1878; 0.1879] 363094.93 100.0%
## Region = Western Asia 6 1.7819 [1.7815; 1.7823] 16848115.86 100.0%
## Region = Northern Africa 4 0.2588 [0.2586; 0.2589] 2181537.78 100.0%
## Region = Southern Africa 1 0.1825 [0.1823; 0.1827] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2179529315.68 12 0
## Within groups 271638567.10 48 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 6.7446 [4.2797; 10.6290] 0.1077 0.3282
## Region = Central and South America and t ... 10 0.3325 [0.1326; 0.8342] 2.2018 1.4838
## Region = Northern Europe 8 1.9588 [1.5732; 2.4390] 0.1001 0.3164
## Region = Eastern Europe 8 0.3578 [0.1453; 0.8809] 1.6910 1.3004
## Region = Southern Europe 6 2.1199 [1.3476; 3.3347] 0.3206 0.5662
## Region = Western Europe 6 3.1091 [2.6544; 3.6416] 0.0390 0.1976
## Region = Australia and New Zealand 2 1.5579 [1.3287; 1.8267] 0.0132 0.1148
## Region = Eastern Asia 4 0.1249 [0.0241; 0.6467] 2.8160 1.6781
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.1281 [0.0377; 0.4353] 0.7793 0.8828
## Region = Southern Asia 2 0.1737 [0.1430; 0.2110] 0.0197 0.1404
## Region = Western Asia 6 0.4939 [0.2193; 1.1124] 1.0298 1.0148
## Region = Northern Africa 4 0.1730 [0.1029; 0.2911] 0.2816 0.5307
## Region = Southern Africa 1 0.1825 [0.1823; 0.1827] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 2747.39 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.5023 [0.5019; 0.5026] 0.3 1.5 Northern Africa
## ARGENTINA 0.6236 [0.6232; 0.6240] 0.4 1.5 Central and South America and the Caribbean
## AUSTRALIA 1.9091 [1.9081; 1.9100] 0.7 1.5 Australia and New Zealand
## AUSTRIA 3.4460 [3.4440; 3.4481] 0.5 1.5 Western Europe
## BELARUS 0.0188 [0.0187; 0.0190] 0.0 1.5 Eastern Europe
## BELGIUM 2.6343 [2.6327; 2.6359] 0.5 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0490 [0.0486; 0.0494] 0.0 1.5 Southern Europe
## BRAZIL 0.1618 [0.1617; 0.1619] 0.6 1.5 Central and South America and the Caribbean
## BULGARIA 0.4892 [0.4884; 0.4901] 0.1 1.5 Eastern Europe
## CANADA 5.8395 [5.8381; 5.8408] 3.5 1.5 Northern America
## CHILE 0.3141 [0.3137; 0.3146] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0053 [0.0053; 0.0053] 0.1 1.5 Eastern Asia
## COLOMBIA 0.0820 [0.0819; 0.0821] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.4773 [0.4762; 0.4783] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.2432 [2.2417; 2.2447] 0.4 1.5 Eastern Europe
## ECUADOR 0.3322 [0.3317; 0.3327] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.3250 [0.3248; 0.3252] 0.5 1.5 Northern Africa
## ESTONIA 0.5211 [0.5191; 0.5231] 0.0 1.5 Northern Europe
## FINLAND 5.7890 [5.7856; 5.7923] 0.5 1.5 Northern Europe
## FRANCE 4.3160 [4.3151; 4.3168] 4.7 1.5 Western Europe
## GERMANY 3.8865 [3.8858; 3.8872] 5.4 1.5 Western Europe
## GREECE 3.0761 [3.0744; 3.0778] 0.6 1.5 Southern Europe
## HUNGARY 1.2037 [1.2026; 1.2048] 0.2 1.5 Eastern Europe
## INDIA 0.2357 [0.2356; 0.2357] 5.1 1.5 Southern Asia
## IRELAND 4.6899 [4.6866; 4.6932] 0.4 1.5 Northern Europe
## ITALY 2.0384 [2.0378; 2.0390] 2.1 1.5 Southern Europe
## JAPAN 1.0988 [1.0985; 1.0991] 2.4 1.5 Eastern Asia
## JORDAN 0.2052 [0.2047; 0.2057] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0285 [0.0283; 0.0286] 0.0 1.5 Central Asia
## KUWAIT 0.2263 [0.2254; 0.2271] 0.0 1.5 Western Asia
## LATVIA 0.6788 [0.6770; 0.6807] 0.0 1.5 Northern Europe
## LEBANON 0.7612 [0.7600; 0.7625] 0.1 1.5 Western Asia
## LITHUANIA 0.7232 [0.7217; 0.7248] 0.0 1.5 Northern Europe
## LUXEMBOURG 3.7951 [3.7863; 3.8039] 0.0 1.5 Western Europe
## MEXICO 0.2666 [0.2664; 0.2668] 0.5 1.5 Central and South America and the Caribbean
## MOROCCO 0.0533 [0.0531; 0.0534] 0.0 1.5 Northern Africa
## NETHERLANDS 2.5304 [2.5291; 2.5317] 0.7 1.5 Western Europe
## NEW ZEALAND 1.6623 [1.6603; 1.6643] 0.1 1.5 Australia and New Zealand
## NORWAY 4.4844 [4.4813; 4.4874] 0.4 1.5 Northern Europe
## PAKISTAN 0.1948 [0.1947; 0.1949] 0.6 1.5 Southern Asia
## PERU 0.0813 [0.0811; 0.0815] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0735 [0.0734; 0.0736] 0.1 1.5 South-eastern Asia
## POLAND 0.2380 [0.2377; 0.2383] 0.2 1.5 Eastern Europe
## PORTUGAL 3.8753 [3.8734; 3.8773] 0.7 1.5 Southern Europe
## PUERTO RICO 8.4500 [8.4450; 8.4550] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 0.7133 [0.7126; 0.7139] 0.3 1.5 Eastern Europe
## RUSSIA 0.1598 [0.1597; 0.1599] 0.4 1.5 Eastern Europe
## SAUDI ARABIA 0.5929 [0.5924; 0.5933] 0.3 1.5 Western Asia
## SERBIA 0.0947 [0.0944; 0.0950] 0.0 1.5 Southern Europe
## SLOVAKIA 2.9752 [2.9728; 2.9776] 0.3 1.5 Eastern Europe
## SLOVENIA 2.2320 [2.2286; 2.2354] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2093 [0.2091; 0.2095] 0.2 1.5 Southern Africa
## SOUTH KOREA 1.3683 [1.3677; 1.3688] 1.2 1.5 Eastern Asia
## SPAIN 5.4072 [5.4061; 5.4083] 4.4 1.5 Southern Europe
## SWEDEN 4.6491 [4.6469; 4.6514] 0.8 1.5 Northern Europe
## SWITZERLAND 2.7352 [2.7333; 2.7371] 0.4 1.5 Western Europe
## TAIWAN 0.2174 [0.2171; 0.2177] 0.1 1.5 Eastern Asia
## THAILAND 0.3580 [0.3578; 0.3583] 0.4 1.5 South-eastern Asia
## TUNISIA 0.2845 [0.2840; 0.2851] 0.1 1.5 Northern Africa
## TÜRKIYE 2.7223 [2.7216; 2.7229] 3.5 1.5 Western Asia
## UNITED ARAB EMIRATES 0.8151 [0.8141; 0.8160] 0.1 1.5 Western Asia
## UNITED KINGDOM 5.6548 [5.6539; 5.6558] 6.3 1.5 Northern Europe
## UNITED STATES 8.7504 [8.7498; 8.7509] 47.2 1.5 Northern America
## URUGUAY 0.6987 [0.6973; 0.7002] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.8805 [0.8799; 0.8810] 0.4 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.1030 [4.1028; 4.1031] 64828.80 0
## Random effects model 0.7013 [0.5117; 0.9613] -2.21 0.0274
##
## Quantifying heterogeneity:
## tau^2 = 1.6824 [1.3038; 3.6877]; tau = 1.2971 [1.1418; 1.9203]
## I^2 = 100.0%; H = 6486.87
##
## Test of heterogeneity:
## Q d.f. p-value
## 2693089052.56 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 8.5100 [8.5095; 8.5105] 11220938.21 100.0%
## Region = Central and South America and t ... 10 0.6189 [0.6188; 0.6191] 116032057.68 100.0%
## Region = Northern Europe 8 5.3564 [5.3556; 5.3572] 8027178.92 100.0%
## Region = Eastern Europe 8 0.7868 [0.7866; 0.7871] 48010225.50 100.0%
## Region = Southern Europe 8 3.7776 [3.7770; 3.7782] 38499842.30 100.0%
## Region = Western Europe 7 3.8214 [3.8210; 3.8219] 6601696.05 100.0%
## Region = Australia and New Zealand 2 1.8709 [1.8701; 1.8718] 43916.07 100.0%
## Region = Eastern Asia 4 0.9503 [0.9501; 0.9505] 80189240.15 100.0%
## Region = Central Asia 1 0.0285 [0.0283; 0.0286] 0.00 --
## Region = South-eastern Asia 2 0.2507 [0.2505; 0.2508] 4984882.61 100.0%
## Region = Southern Asia 2 0.2309 [0.2308; 0.2309] 423047.99 100.0%
## Region = Western Asia 6 2.2367 [2.2362; 2.2372] 22656268.47 100.0%
## Region = Northern Africa 4 0.3548 [0.3546; 0.3550] 3237663.07 100.0%
## Region = Southern Africa 1 0.2093 [0.2091; 0.2095] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2353162095.54 13 0
## Within groups 339926957.02 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 7.1483 [4.8091; 10.6252] 0.0818 0.2860
## Region = Central and South America and t ... 10 0.3970 [0.1544; 1.0207] 2.3212 1.5235
## Region = Northern Europe 8 2.3129 [1.8457; 2.8984] 0.1060 0.3256
## Region = Eastern Europe 8 0.4601 [0.1938; 1.0920] 1.5560 1.2474
## Region = Southern Europe 8 0.9475 [0.6185; 1.4517] 0.3790 0.6156
## Region = Western Europe 7 3.2702 [2.8245; 3.7862] 0.0391 0.1978
## Region = Australia and New Zealand 2 1.7814 [1.5554; 2.0402] 0.0096 0.0979
## Region = Eastern Asia 4 0.2040 [0.0512; 0.8124] 1.9877 1.4099
## Region = Central Asia 1 0.0285 [0.0283; 0.0286] -- --
## Region = South-eastern Asia 2 0.1622 [0.0344; 0.7657] 1.2541 1.1199
## Region = Southern Asia 2 0.2143 [0.1777; 0.2583] 0.0182 0.1348
## Region = Western Asia 6 0.5996 [0.2565; 1.4017] 1.1262 1.0612
## Region = Northern Africa 4 0.2230 [0.1296; 0.3839] 0.3071 0.5542
## Region = Southern Africa 1 0.2093 [0.2091; 0.2095] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 657578.49 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.5436 [0.5432; 0.5440] 0.3 1.5 Northern Africa
## ARGENTINA 0.8075 [0.8071; 0.8080] 0.5 1.5 Central and South America and the Caribbean
## AUSTRALIA 2.1429 [2.1419; 2.1439] 0.8 1.5 Australia and New Zealand
## AUSTRIA 3.8892 [3.8870; 3.8914] 0.5 1.5 Western Europe
## BELARUS 0.0209 [0.0208; 0.0211] 0.0 1.5 Eastern Europe
## BELGIUM 2.8197 [2.8180; 2.8213] 0.5 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1042 [0.1036; 0.1047] 0.0 1.5 Southern Europe
## BRAZIL 0.2003 [0.2002; 0.2004] 0.6 1.5 Central and South America and the Caribbean
## BULGARIA 0.3512 [0.3505; 0.3520] 0.0 1.5 Eastern Europe
## CANADA 6.4490 [6.4476; 6.4504] 3.6 1.5 Northern America
## CHILE 0.4029 [0.4024; 0.4033] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0100 [0.0100; 0.0101] 0.2 1.5 Eastern Asia
## COLOMBIA 0.0904 [0.0902; 0.0905] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.5290 [0.5279; 0.5302] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.4462 [2.4447; 2.4478] 0.4 1.5 Eastern Europe
## ECUADOR 0.3715 [0.3710; 0.3720] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.4519 [0.4517; 0.4522] 0.6 1.5 Northern Africa
## ESTONIA 0.6499 [0.6477; 0.6522] 0.0 1.5 Northern Europe
## FINLAND 6.2340 [6.2305; 6.2375] 0.5 1.5 Northern Europe
## FRANCE 4.6399 [4.6391; 4.6408] 4.8 1.5 Western Europe
## GERMANY 4.1898 [4.1890; 4.1905] 5.5 1.5 Western Europe
## GREECE 2.9461 [2.9444; 2.9478] 0.5 1.5 Southern Europe
## HUNGARY 1.3818 [1.3806; 1.3831] 0.2 1.5 Eastern Europe
## INDIA 0.2661 [0.2661; 0.2662] 5.4 1.5 Southern Asia
## IRELAND 5.7487 [5.7451; 5.7523] 0.4 1.5 Northern Europe
## ITALY 2.1488 [2.1482; 2.1494] 2.1 1.5 Southern Europe
## JAPAN 1.7888 [1.7885; 1.7892] 3.7 1.5 Eastern Asia
## JORDAN 0.2711 [0.2705; 0.2717] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0437 [0.0436; 0.0439] 0.0 1.5 Central Asia
## KUWAIT 0.3421 [0.3411; 0.3431] 0.0 1.5 Western Asia
## LATVIA 0.8875 [0.8853; 0.8896] 0.0 1.5 Northern Europe
## LEBANON 0.7933 [0.7921; 0.7945] 0.1 1.5 Western Asia
## LITHUANIA 0.8457 [0.8440; 0.8474] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.0584 [4.0494; 4.0674] 0.0 1.5 Western Europe
## MEXICO 0.2671 [0.2669; 0.2672] 0.5 1.5 Central and South America and the Caribbean
## MOROCCO 0.0653 [0.0651; 0.0654] 0.0 1.5 Northern Africa
## NETHERLANDS 2.8475 [2.8461; 2.8488] 0.8 1.5 Western Europe
## NEW ZEALAND 1.9467 [1.9446; 1.9489] 0.1 1.5 Australia and New Zealand
## NORWAY 4.8179 [4.8147; 4.8211] 0.4 1.5 Northern Europe
## PAKISTAN 0.2124 [0.2123; 0.2125] 0.6 1.5 Southern Asia
## PERU 0.0954 [0.0952; 0.0955] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0780 [0.0779; 0.0781] 0.1 1.5 South-eastern Asia
## POLAND 0.2180 [0.2177; 0.2182] 0.1 1.5 Eastern Europe
## PORTUGAL 4.3551 [4.3530; 4.3572] 0.7 1.5 Southern Europe
## PUERTO RICO 9.4847 [9.4794; 9.4900] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 0.7460 [0.7453; 0.7466] 0.2 1.5 Eastern Europe
## RUSSIA 0.3209 [0.3208; 0.3211] 0.7 1.5 Eastern Europe
## SAUDI ARABIA 0.7378 [0.7373; 0.7383] 0.3 1.5 Western Asia
## SERBIA 0.1341 [0.1337; 0.1345] 0.0 1.5 Southern Europe
## SLOVAKIA 3.2284 [3.2259; 3.2309] 0.3 1.5 Eastern Europe
## SLOVENIA 2.4452 [2.4416; 2.4487] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2379 [0.2377; 0.2381] 0.2 1.5 Southern Africa
## SOUTH KOREA 1.7305 [1.7299; 1.7311] 1.4 1.5 Eastern Asia
## SPAIN 5.7247 [5.7236; 5.7258] 4.4 1.5 Southern Europe
## SWEDEN 4.7299 [4.7276; 4.7322] 0.7 1.5 Northern Europe
## SWITZERLAND 2.9603 [2.9584; 2.9623] 0.4 1.5 Western Europe
## TAIWAN 0.2224 [0.2221; 0.2227] 0.1 1.5 Eastern Asia
## THAILAND 0.4123 [0.4121; 0.4126] 0.5 1.5 South-eastern Asia
## TUNISIA 0.4017 [0.4011; 0.4024] 0.1 1.5 Northern Africa
## TÜRKIYE 3.1476 [3.1470; 3.1483] 3.8 1.5 Western Asia
## UNITED ARAB EMIRATES 0.3576 [0.3569; 0.3582] 0.1 1.5 Western Asia
## UNITED KINGDOM 6.6897 [6.6887; 6.6908] 7.0 1.5 Northern Europe
## UNITED STATES 8.4694 [8.4688; 8.4699] 43.0 1.5 Northern America
## URUGUAY 1.0938 [1.0920; 1.0957] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 1.0497 [1.0491; 1.0503] 0.5 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.0158 [4.0156; 4.0160] 66064.07 0
## Random effects model 0.8148 [0.6018; 1.1031] -1.33 0.1851
##
## Quantifying heterogeneity:
## tau^2 = 1.5528 [1.2465; 3.4623]; tau = 1.2461 [1.1165; 1.8607]
## I^2 = 100.0%; H = 6597.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 2785305996.51 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 8.2911 [8.2906; 8.2916] 5632589.64 100.0%
## Region = Central and South America and t ... 10 0.7169 [0.7168; 0.7171] 131087456.32 100.0%
## Region = Northern Europe 8 6.2225 [6.2216; 6.2233] 10598553.38 100.0%
## Region = Eastern Europe 8 0.8240 [0.8238; 0.8243] 46532134.40 100.0%
## Region = Southern Europe 8 3.9860 [3.9854; 3.9866] 42713840.25 100.0%
## Region = Western Europe 7 4.1278 [4.1273; 4.1283] 6567298.08 100.0%
## Region = Australia and New Zealand 2 2.1122 [2.1113; 2.1131] 24887.10 100.0%
## Region = Eastern Asia 4 1.3853 [1.3851; 1.3856] 136650254.52 100.0%
## Region = Central Asia 1 0.0437 [0.0436; 0.0439] 0.00 --
## Region = South-eastern Asia 2 0.2891 [0.2889; 0.2892] 6039855.91 100.0%
## Region = Southern Asia 2 0.2599 [0.2598; 0.2599] 659768.68 100.0%
## Region = Western Asia 6 2.5887 [2.5882; 2.5892] 28374774.43 100.0%
## Region = Northern Africa 4 0.4454 [0.4452; 0.4456] 3237163.16 100.0%
## Region = Southern Africa 1 0.2379 [0.2377; 0.2381] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2367187420.63 13 0
## Within groups 418118575.88 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 7.3905 [5.6583; 9.6529] 0.0371 0.1927
## Region = Central and South America and t ... 10 0.4768 [0.1896; 1.1990] 2.2140 1.4880
## Region = Northern Europe 8 2.6805 [2.0952; 3.4292] 0.1264 0.3555
## Region = Eastern Europe 8 0.5044 [0.2321; 1.0959] 1.2542 1.1199
## Region = Southern Europe 8 1.1400 [0.7364; 1.7647] 0.3976 0.6306
## Region = Western Europe 7 3.5632 [3.0980; 4.0983] 0.0357 0.1889
## Region = Australia and New Zealand 2 2.0425 [1.8590; 2.2440] 0.0046 0.0679
## Region = Eastern Asia 4 0.2884 [0.0629; 1.3212] 2.4122 1.5531
## Region = Central Asia 1 0.0437 [0.0436; 0.0439] -- --
## Region = South-eastern Asia 2 0.1794 [0.0351; 0.9167] 1.3854 1.1770
## Region = Southern Asia 2 0.2378 [0.1907; 0.2965] 0.0254 0.1593
## Region = Western Asia 6 0.6276 [0.2510; 1.5694] 1.3121 1.1455
## Region = Northern Africa 4 0.2833 [0.1744; 0.4603] 0.2453 0.4953
## Region = Southern Africa 1 0.2379 [0.2377; 0.2381] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 730154.52 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.6123 [0.6119; 0.6128] 0.3 1.5 Northern Africa
## ARGENTINA 0.9655 [0.9650; 0.9660] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 3.9071 [3.9058; 3.9084] 1.3 1.5 Australia and New Zealand
## AUSTRIA 4.2035 [4.2012; 4.2058] 0.5 1.5 Western Europe
## BELARUS 0.0344 [0.0342; 0.0346] 0.0 1.5 Eastern Europe
## BELGIUM 2.8712 [2.8696; 2.8729] 0.4 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1485 [0.1479; 0.1492] 0.0 1.5 Southern Europe
## BRAZIL 0.2464 [0.2463; 0.2465] 0.7 1.5 Central and South America and the Caribbean
## BULGARIA 0.4812 [0.4804; 0.4820] 0.0 1.5 Eastern Europe
## CANADA 7.3987 [7.3972; 7.4002] 3.7 1.5 Northern America
## CHILE 0.4888 [0.4882; 0.4893] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0143 [0.0143; 0.0143] 0.3 1.5 Eastern Asia
## COLOMBIA 0.1060 [0.1058; 0.1062] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.5611 [0.5599; 0.5622] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.8554 [2.8537; 2.8570] 0.4 1.5 Eastern Europe
## ECUADOR 0.3806 [0.3801; 0.3811] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.5793 [0.5791; 0.5796] 0.7 1.5 Northern Africa
## ESTONIA 0.8479 [0.8453; 0.8505] 0.0 1.5 Northern Europe
## FINLAND 6.7770 [6.7733; 6.7806] 0.5 1.5 Northern Europe
## FRANCE 4.9101 [4.9092; 4.9110] 4.4 1.5 Western Europe
## GERMANY 4.4645 [4.4637; 4.4653] 5.1 1.5 Western Europe
## GREECE 3.2543 [3.2525; 3.2561] 0.5 1.5 Southern Europe
## HUNGARY 1.6067 [1.6054; 1.6080] 0.2 1.5 Eastern Europe
## INDIA 0.2922 [0.2921; 0.2922] 5.2 1.5 Southern Asia
## IRELAND 6.9816 [6.9776; 6.9855] 0.5 1.5 Northern Europe
## ITALY 2.2892 [2.2886; 2.2899] 1.9 1.5 Southern Europe
## JAPAN 2.3349 [2.3344; 2.3353] 4.2 1.5 Eastern Asia
## JORDAN 0.4185 [0.4177; 0.4192] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0488 [0.0487; 0.0490] 0.0 1.5 Central Asia
## KUWAIT 0.7306 [0.7291; 0.7321] 0.0 1.5 Western Asia
## LATVIA 1.1425 [1.1401; 1.1449] 0.0 1.5 Northern Europe
## LEBANON 0.8309 [0.8297; 0.8321] 0.1 1.5 Western Asia
## LITHUANIA 1.0202 [1.0183; 1.0221] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.0818 [4.0729; 4.0907] 0.0 1.5 Western Europe
## MEXICO 0.2726 [0.2724; 0.2727] 0.5 1.5 Central and South America and the Caribbean
## MOROCCO 0.0781 [0.0780; 0.0783] 0.0 1.5 Northern Africa
## NETHERLANDS 3.0818 [3.0804; 3.0832] 0.7 1.5 Western Europe
## NEW ZEALAND 2.2133 [2.2110; 2.2155] 0.1 1.5 Australia and New Zealand
## NORWAY 4.9931 [4.9899; 4.9963] 0.4 1.5 Northern Europe
## PAKISTAN 0.2292 [0.2291; 0.2293] 0.6 1.5 Southern Asia
## PERU 0.1101 [0.1099; 0.1103] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0914 [0.0913; 0.0915] 0.1 1.5 South-eastern Asia
## POLAND 0.2820 [0.2817; 0.2822] 0.2 1.5 Eastern Europe
## PORTUGAL 4.3602 [4.3581; 4.3623] 0.6 1.5 Southern Europe
## PUERTO RICO 9.6518 [9.6464; 9.6572] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 0.8871 [0.8865; 0.8878] 0.3 1.5 Eastern Europe
## RUSSIA 0.7291 [0.7288; 0.7293] 1.5 1.5 Eastern Europe
## SAUDI ARABIA 1.0581 [1.0575; 1.0587] 0.4 1.5 Western Asia
## SERBIA 0.2477 [0.2471; 0.2482] 0.0 1.5 Southern Europe
## SLOVAKIA 3.4486 [3.4460; 3.4511] 0.3 1.5 Eastern Europe
## SLOVENIA 2.6081 [2.6045; 2.6118] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2529 [0.2527; 0.2532] 0.2 1.5 Southern Africa
## SOUTH KOREA 1.8725 [1.8719; 1.8731] 1.3 1.5 Eastern Asia
## SPAIN 6.0784 [6.0772; 6.0795] 4.0 1.5 Southern Europe
## SWEDEN 5.1810 [5.1786; 5.1834] 0.7 1.5 Northern Europe
## SWITZERLAND 3.1810 [3.1789; 3.1830] 0.4 1.5 Western Europe
## TAIWAN 0.2939 [0.2935; 0.2942] 0.1 1.5 Eastern Asia
## THAILAND 0.5597 [0.5594; 0.5600] 0.5 1.5 South-eastern Asia
## TUNISIA 0.4665 [0.4658; 0.4671] 0.1 1.5 Northern Africa
## TÜRKIYE 3.5727 [3.5720; 3.5734] 3.8 1.5 Western Asia
## UNITED ARAB EMIRATES 0.5663 [0.5655; 0.5671] 0.1 1.5 Western Asia
## UNITED KINGDOM 8.0419 [8.0408; 8.0431] 7.3 1.5 Northern Europe
## UNITED STATES 9.5958 [9.5952; 9.5963] 42.5 1.5 Northern America
## URUGUAY 1.1789 [1.1770; 1.1809] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 1.2948 [1.2941; 1.2954] 0.5 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.4758 [4.4756; 4.4760] 76511.88 0
## Random effects model 0.9846 [0.7279; 1.3317] -0.10 0.9195
##
## Quantifying heterogeneity:
## tau^2 = 1.5434 [1.1932; 3.2855]; tau = 1.2423 [1.0923; 1.8126]
## I^2 = 100.0%; H = 7085.04
##
## Test of heterogeneity:
## Q d.f. p-value
## 3212654700.07 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 9.4002 [9.3996; 9.4007] 5937916.26 100.0%
## Region = Central and South America and t ... 10 0.7774 [0.7773; 0.7776] 133383413.51 100.0%
## Region = Northern Europe 8 7.3753 [7.3743; 7.3762] 14507032.36 100.0%
## Region = Eastern Europe 8 1.0490 [1.0488; 1.0493] 36007055.41 100.0%
## Region = Southern Europe 8 4.1939 [4.1933; 4.1945] 46228063.10 100.0%
## Region = Western Europe 7 4.3859 [4.3854; 4.3864] 6962819.11 100.0%
## Region = Australia and New Zealand 2 3.6930 [3.6918; 3.6942] 1062843.73 100.0%
## Region = Eastern Asia 4 1.6880 [1.6878; 1.6883] 185013231.84 100.0%
## Region = Central Asia 1 0.0488 [0.0487; 0.0490] 0.00 --
## Region = South-eastern Asia 2 0.3956 [0.3954; 0.3958] 8764573.50 100.0%
## Region = Southern Asia 2 0.2848 [0.2848; 0.2849] 845662.66 100.0%
## Region = Western Asia 6 2.8948 [2.8943; 2.8954] 30665365.81 100.0%
## Region = Northern Africa 4 0.5446 [0.5444; 0.5448] 3860815.37 100.0%
## Region = Southern Africa 1 0.2529 [0.2527; 0.2532] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2739415907.42 13 0
## Within groups 473238792.65 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 8.4259 [6.5306; 10.8713] 0.0338 0.1839
## Region = Central and South America and t ... 10 0.5391 [0.2262; 1.2850] 1.9638 1.4014
## Region = Northern Europe 8 3.1514 [2.4032; 4.1325] 0.1530 0.3911
## Region = Eastern Europe 8 0.6837 [0.3806; 1.2280] 0.7143 0.8452
## Region = Southern Europe 8 1.3437 [0.8645; 2.0886] 0.4051 0.6365
## Region = Western Europe 7 3.7581 [3.2691; 4.3202] 0.0354 0.1882
## Region = Australia and New Zealand 2 2.9407 [1.6849; 5.1325] 0.1615 0.4019
## Region = Eastern Asia 4 0.3681 [0.0728; 1.8624] 2.7368 1.6543
## Region = Central Asia 1 0.0488 [0.0487; 0.0490] -- --
## Region = South-eastern Asia 2 0.2261 [0.0383; 1.3361] 1.6429 1.2817
## Region = Southern Asia 2 0.2588 [0.2039; 0.3283] 0.0295 0.1718
## Region = Western Asia 6 0.9035 [0.4088; 1.9965] 0.9819 0.9909
## Region = Northern Africa 4 0.3372 [0.2079; 0.5469] 0.2434 0.4934
## Region = Southern Africa 1 0.2529 [0.2527; 0.2532] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 776891.62 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.8777 [ 0.8772; 0.8782] 0.4 1.5 Northern Africa
## ARGENTINA 1.0803 [ 1.0798; 1.0808] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 6.1076 [ 6.1060; 6.1093] 1.8 1.5 Australia and New Zealand
## AUSTRIA 4.5047 [ 4.5024; 4.5071] 0.5 1.5 Western Europe
## BELARUS 0.0451 [ 0.0449; 0.0453] 0.0 1.5 Eastern Europe
## BELGIUM 3.1127 [ 3.1110; 3.1144] 0.4 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1600 [ 0.1593; 0.1607] 0.0 1.5 Southern Europe
## BRAZIL 0.2972 [ 0.2971; 0.2973] 0.8 1.5 Central and South America and the Caribbean
## BULGARIA 0.5914 [ 0.5905; 0.5924] 0.1 1.5 Eastern Europe
## CANADA 8.1048 [ 8.1032; 8.1063] 3.6 1.5 Northern America
## CHILE 0.5859 [ 0.5853; 0.5865] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0183 [ 0.0183; 0.0184] 0.3 1.5 Eastern Asia
## COLOMBIA 0.1232 [ 0.1230; 0.1233] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.6205 [ 0.6193; 0.6218] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 3.2558 [ 3.2540; 3.2576] 0.4 1.5 Eastern Europe
## ECUADOR 0.4213 [ 0.4207; 0.4218] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.7263 [ 0.7261; 0.7266] 0.8 1.5 Northern Africa
## ESTONIA 1.0802 [ 1.0772; 1.0831] 0.0 1.5 Northern Europe
## FINLAND 6.5386 [ 6.5351; 6.5422] 0.4 1.5 Northern Europe
## FRANCE 5.2787 [ 5.2778; 5.2797] 4.2 1.5 Western Europe
## GERMANY 4.7110 [ 4.7102; 4.7117] 4.8 1.5 Western Europe
## GREECE 3.2833 [ 3.2815; 3.2851] 0.4 1.5 Southern Europe
## HUNGARY 1.7894 [ 1.7880; 1.7908] 0.2 1.5 Eastern Europe
## INDIA 0.3310 [ 0.3309; 0.3310] 5.4 1.5 Southern Asia
## IRELAND 7.5993 [ 7.5952; 7.6035] 0.4 1.5 Northern Europe
## ITALY 2.4579 [ 2.4572; 2.4585] 1.9 1.5 Southern Europe
## JAPAN 2.9447 [ 2.9442; 2.9452] 4.7 1.5 Eastern Asia
## JORDAN 0.5784 [ 0.5776; 0.5793] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0671 [ 0.0669; 0.0673] 0.0 1.5 Central Asia
## KUWAIT 0.6781 [ 0.6767; 0.6794] 0.0 1.5 Western Asia
## LATVIA 1.3962 [ 1.3935; 1.3989] 0.0 1.5 Northern Europe
## LEBANON 0.9179 [ 0.9167; 0.9192] 0.1 1.5 Western Asia
## LITHUANIA 1.2046 [ 1.2026; 1.2067] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.2040 [ 4.1951; 4.2129] 0.0 1.5 Western Europe
## MEXICO 0.2951 [ 0.2949; 0.2953] 0.4 1.5 Central and South America and the Caribbean
## MOROCCO 0.0859 [ 0.0858; 0.0861] 0.0 1.5 Northern Africa
## NETHERLANDS 3.3712 [ 3.3698; 3.3727] 0.7 1.5 Western Europe
## NEW ZEALAND 2.5248 [ 2.5224; 2.5272] 0.1 1.5 Australia and New Zealand
## NORWAY 5.0716 [ 5.0683; 5.0748] 0.3 1.5 Northern Europe
## PAKISTAN 0.2485 [ 0.2483; 0.2486] 0.6 1.5 Southern Asia
## PERU 0.1205 [ 0.1203; 0.1207] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0950 [ 0.0949; 0.0951] 0.1 1.5 South-eastern Asia
## POLAND 0.3720 [ 0.3717; 0.3723] 0.2 1.5 Eastern Europe
## PORTUGAL 4.8798 [ 4.8776; 4.8821] 0.6 1.5 Southern Europe
## PUERTO RICO 11.3900 [11.3841; 11.3959] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 1.0166 [ 1.0159; 1.0174] 0.3 1.5 Eastern Europe
## RUSSIA 0.9373 [ 0.9370; 0.9376] 1.7 1.5 Eastern Europe
## SAUDI ARABIA 1.3930 [ 1.3923; 1.3936] 0.5 1.5 Western Asia
## SERBIA 0.3969 [ 0.3962; 0.3976] 0.0 1.5 Southern Europe
## SLOVAKIA 3.7363 [ 3.7336; 3.7390] 0.3 1.5 Eastern Europe
## SLOVENIA 2.9582 [ 2.9543; 2.9621] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2727 [ 0.2724; 0.2729] 0.2 1.5 Southern Africa
## SOUTH KOREA 2.0337 [ 2.0331; 2.0344] 1.3 1.5 Eastern Asia
## SPAIN 6.3670 [ 6.3658; 6.3682] 3.7 1.5 Southern Europe
## SWEDEN 5.6087 [ 5.6063; 5.6112] 0.7 1.5 Northern Europe
## SWITZERLAND 3.4305 [ 3.4284; 3.4326] 0.4 1.5 Western Europe
## TAIWAN 0.3606 [ 0.3602; 0.3610] 0.1 1.5 Eastern Asia
## THAILAND 0.6334 [ 0.6331; 0.6337] 0.5 1.5 South-eastern Asia
## TUNISIA 0.5206 [ 0.5199; 0.5213] 0.1 1.5 Northern Africa
## TÜRKIYE 4.0914 [ 4.0907; 4.0922] 4.0 1.5 Western Asia
## UNITED ARAB EMIRATES 0.8103 [ 0.8094; 0.8113] 0.1 1.5 Western Asia
## UNITED KINGDOM 9.5420 [ 9.5407; 9.5432] 7.8 1.5 Northern Europe
## UNITED STATES 10.2777 [10.2771; 10.2783] 41.0 1.5 Northern America
## URUGUAY 1.3733 [ 1.3712; 1.3753] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 1.5927 [ 1.5919; 1.5934] 0.6 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.8238 [4.8237; 4.8240] 85000.72 0
## Random effects model 1.1390 [0.8457; 1.5340] 0.86 0.3915
##
## Quantifying heterogeneity:
## tau^2 = 1.4996 [1.1600; 3.1687]; tau = 1.2246 [1.0770; 1.7801]
## I^2 = 100.0%; H = 7444.25
##
## Test of heterogeneity:
## Q d.f. p-value
## 3546674633.79 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 10.0816 [10.0810; 10.0821] 5473234.57 100.0%
## Region = Central and South America and t ... 10 0.9097 [ 0.9096; 0.9099] 158083122.85 100.0%
## Region = Northern Europe 8 8.5849 [ 8.5839; 8.5859] 20615913.66 100.0%
## Region = Eastern Europe 8 1.2323 [ 1.2320; 1.2325] 35846862.79 100.0%
## Region = Southern Europe 8 4.4051 [ 4.4045; 4.4057] 48295863.53 100.0%
## Region = Western Europe 7 4.6796 [ 4.6791; 4.6801] 7181921.65 100.0%
## Region = Australia and New Zealand 2 5.7207 [ 5.7192; 5.7222] 3043431.34 100.0%
## Region = Eastern Asia 4 2.0502 [ 2.0499; 2.0505] 236020621.17 100.0%
## Region = Central Asia 1 0.0671 [ 0.0669; 0.0673] 0.00 --
## Region = South-eastern Asia 2 0.4497 [ 0.4495; 0.4499] 10286375.38 100.0%
## Region = Southern Asia 2 0.3215 [ 0.3214; 0.3215] 1309818.65 100.0%
## Region = Western Asia 6 3.3000 [ 3.2994; 3.3005] 33851780.93 100.0%
## Region = Northern Africa 4 0.7149 [ 0.7147; 0.7151] 5559722.82 100.0%
## Region = Southern Africa 1 0.2727 [ 0.2724; 0.2729] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2981105964.46 13 0
## Within groups 565568669.34 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 9.1268 [7.2315; 11.5188] 0.0282 0.1680
## Region = Central and South America and t ... 10 0.6218 [0.2595; 1.4902] 1.9883 1.4101
## Region = Northern Europe 8 3.5371 [2.5886; 4.8332] 0.2030 0.4505
## Region = Eastern Europe 8 0.8207 [0.4790; 1.4064] 0.6040 0.7772
## Region = Southern Europe 8 1.5249 [0.9866; 2.3570] 0.3949 0.6284
## Region = Western Europe 7 4.0196 [3.5069; 4.6072] 0.0339 0.1842
## Region = Australia and New Zealand 2 3.9269 [1.6523; 9.3329] 0.3902 0.6246
## Region = Eastern Asia 4 0.4462 [0.0818; 2.4338] 2.9970 1.7312
## Region = Central Asia 1 0.0671 [0.0669; 0.0673] -- --
## Region = South-eastern Asia 2 0.2453 [0.0382; 1.5742] 1.7990 1.3413
## Region = Southern Asia 2 0.2868 [0.2165; 0.3798] 0.0411 0.2028
## Region = Western Asia 6 1.0884 [0.5255; 2.2544] 0.8281 0.9100
## Region = Northern Africa 4 0.4110 [0.2484; 0.6800] 0.2640 0.5138
## Region = Southern Africa 1 0.2727 [0.2724; 0.2729] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 776190.39 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 1.0435 [ 1.0430; 1.0440] 0.5 1.5 Northern Africa
## ARGENTINA 1.2359 [ 1.2353; 1.2364] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 7.5188 [ 7.5170; 7.5207] 2.0 1.5 Australia and New Zealand
## AUSTRIA 4.6332 [ 4.6308; 4.6355] 0.4 1.5 Western Europe
## BELARUS 0.0609 [ 0.0607; 0.0612] 0.0 1.5 Eastern Europe
## BELGIUM 3.4623 [ 3.4605; 3.4641] 0.4 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.2186 [ 0.2178; 0.2194] 0.0 1.5 Southern Europe
## BRAZIL 0.3671 [ 0.3669; 0.3672] 0.8 1.5 Central and South America and the Caribbean
## BULGARIA 0.6868 [ 0.6858; 0.6878] 0.1 1.5 Eastern Europe
## CANADA 8.9837 [ 8.9821; 8.9854] 3.6 1.5 Northern America
## CHILE 0.6971 [ 0.6964; 0.6977] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0236 [ 0.0236; 0.0236] 0.4 1.5 Eastern Asia
## COLOMBIA 0.1650 [ 0.1648; 0.1652] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.7200 [ 0.7187; 0.7214] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 3.8896 [ 3.8876; 3.8915] 0.5 1.5 Eastern Europe
## ECUADOR 0.4820 [ 0.4815; 0.4826] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.9495 [ 0.9492; 0.9498] 1.0 1.5 Northern Africa
## ESTONIA 1.3609 [ 1.3576; 1.3642] 0.0 1.5 Northern Europe
## FINLAND 6.6345 [ 6.6310; 6.6381] 0.4 1.5 Northern Europe
## FRANCE 5.5188 [ 5.5179; 5.5198] 4.0 1.5 Western Europe
## GERMANY 4.9624 [ 4.9616; 4.9632] 4.5 1.5 Western Europe
## GREECE 3.3903 [ 3.3884; 3.3921] 0.4 1.5 Southern Europe
## HUNGARY 1.7720 [ 1.7706; 1.7734] 0.2 1.5 Eastern Europe
## INDIA 0.3751 [ 0.3750; 0.3751] 5.5 1.5 Southern Asia
## IRELAND 9.3143 [ 9.3098; 9.3189] 0.5 1.5 Northern Europe
## ITALY 2.5593 [ 2.5587; 2.5600] 1.7 1.5 Southern Europe
## JAPAN 3.2023 [ 3.2018; 3.2029] 4.6 1.5 Eastern Asia
## JORDAN 0.5796 [ 0.5788; 0.5804] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0902 [ 0.0899; 0.0904] 0.0 1.5 Central Asia
## KUWAIT 0.7833 [ 0.7818; 0.7847] 0.0 1.5 Western Asia
## LATVIA 1.6802 [ 1.6772; 1.6832] 0.0 1.5 Northern Europe
## LEBANON 1.0485 [ 1.0472; 1.0498] 0.1 1.5 Western Asia
## LITHUANIA 1.2834 [ 1.2812; 1.2855] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.1852 [ 4.1764; 4.1940] 0.0 1.5 Western Europe
## MEXICO 0.3186 [ 0.3184; 0.3187] 0.4 1.5 Central and South America and the Caribbean
## MOROCCO 0.1033 [ 0.1032; 0.1035] 0.0 1.5 Northern Africa
## NETHERLANDS 3.5567 [ 3.5552; 3.5582] 0.7 1.5 Western Europe
## NEW ZEALAND 2.9494 [ 2.9468; 2.9520] 0.2 1.5 Australia and New Zealand
## NORWAY 5.6805 [ 5.6771; 5.6839] 0.3 1.5 Northern Europe
## PAKISTAN 0.2817 [ 0.2816; 0.2818] 0.6 1.5 Southern Asia
## PERU 0.1993 [ 0.1991; 0.1996] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1056 [ 0.1055; 0.1057] 0.1 1.5 South-eastern Asia
## POLAND 0.4692 [ 0.4688; 0.4696] 0.2 1.5 Eastern Europe
## PORTUGAL 5.0680 [ 5.0657; 5.0703] 0.6 1.5 Southern Europe
## PUERTO RICO 12.9825 [12.9762; 12.9889] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 1.1237 [ 1.1229; 1.1244] 0.3 1.5 Eastern Europe
## RUSSIA 0.8413 [ 0.8411; 0.8416] 1.4 1.5 Eastern Europe
## SAUDI ARABIA 1.2320 [ 1.2313; 1.2326] 0.4 1.5 Western Asia
## SERBIA 0.7818 [ 0.7808; 0.7827] 0.1 1.5 Southern Europe
## SLOVAKIA 4.1169 [ 4.1141; 4.1198] 0.3 1.5 Eastern Europe
## SLOVENIA 3.2621 [ 3.2581; 3.2662] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2909 [ 0.2907; 0.2912] 0.2 1.5 Southern Africa
## SOUTH KOREA 2.1810 [ 2.1803; 2.1817] 1.2 1.5 Eastern Asia
## SPAIN 6.4617 [ 6.4605; 6.4629] 3.4 1.5 Southern Europe
## SWEDEN 6.0197 [ 6.0172; 6.0223] 0.7 1.5 Northern Europe
## SWITZERLAND 3.7244 [ 3.7222; 3.7265] 0.3 1.5 Western Europe
## TAIWAN 0.4099 [ 0.4094; 0.4103] 0.1 1.5 Eastern Asia
## THAILAND 0.6688 [ 0.6685; 0.6691] 0.5 1.5 South-eastern Asia
## TUNISIA 0.5660 [ 0.5653; 0.5668] 0.1 1.5 Northern Africa
## TÜRKIYE 4.6908 [ 4.6900; 4.6916] 4.1 1.5 Western Asia
## UNITED ARAB EMIRATES 1.0947 [ 1.0936; 1.0958] 0.1 1.5 Western Asia
## UNITED KINGDOM 11.0046 [11.0033; 11.0059] 8.1 1.5 Northern Europe
## UNITED STATES 11.6703 [11.6696; 11.6709] 41.8 1.5 Northern America
## URUGUAY 1.4897 [ 1.4876; 1.4919] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 1.0882 [ 1.0875; 1.0888] 0.4 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.3991 [5.3989; 5.3993] 96410.91 0
## Random effects model 1.2931 [0.9533; 1.7539] 1.65 0.0984
##
## Quantifying heterogeneity:
## tau^2 = 1.5725 [1.1645; 3.1814]; tau = 1.2540 [1.0791; 1.7836]
## I^2 = 100.0%; H = 8034.92
##
## Test of heterogeneity:
## Q d.f. p-value
## 4131832821.55 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 11.4299 [11.4293; 11.4305] 7447937.33 100.0%
## Region = Central and South America and t ... 10 0.9149 [ 0.9148; 0.9151] 169090037.93 100.0%
## Region = Northern Europe 8 9.8630 [ 9.8619; 9.8641] 25954476.21 100.0%
## Region = Eastern Europe 8 1.2840 [ 1.2837; 1.2842] 47624760.96 100.0%
## Region = Southern Europe 8 4.4639 [ 4.4633; 4.4645] 48035137.06 100.0%
## Region = Western Europe 7 4.9182 [ 4.9177; 4.9187] 6754573.35 100.0%
## Region = Australia and New Zealand 2 7.0400 [ 7.0384; 7.0416] 4047256.53 100.0%
## Region = Eastern Asia 4 2.1464 [ 2.1461; 2.1467] 280350118.38 100.0%
## Region = Central Asia 1 0.0902 [ 0.0899; 0.0904] 0.00 --
## Region = South-eastern Asia 2 0.4709 [ 0.4707; 0.4711] 10868778.15 100.0%
## Region = Southern Asia 2 0.3642 [ 0.3642; 0.3643] 1509620.90 100.0%
## Region = Western Asia 6 3.7762 [ 3.7756; 3.7768] 43627316.73 100.0%
## Region = Northern Africa 4 0.9010 [ 0.9007; 0.9012] 7049513.42 100.0%
## Region = Southern Africa 1 0.2909 [ 0.2907; 0.2912] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 3479473294.60 13 0
## Within groups 652359526.95 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 10.2393 [7.9236; 13.2318] 0.0342 0.1850
## Region = Central and South America and t ... 10 0.7123 [0.2990; 1.6967] 1.9613 1.4004
## Region = Northern Europe 8 4.0203 [2.8863; 5.5997] 0.2287 0.4782
## Region = Eastern Europe 8 0.9230 [0.5078; 1.6775] 0.7434 0.8622
## Region = Southern Europe 8 1.8078 [1.1829; 2.7627] 0.3746 0.6120
## Region = Western Europe 7 4.2331 [3.7225; 4.8136] 0.0301 0.1735
## Region = Australia and New Zealand 2 4.7091 [1.8822; 11.7821] 0.4379 0.6617
## Region = Eastern Asia 4 0.5098 [0.0889; 2.9247] 3.1774 1.7825
## Region = Central Asia 1 0.0902 [0.0899; 0.0904] -- --
## Region = South-eastern Asia 2 0.2657 [0.0435; 1.6221] 1.7037 1.3053
## Region = Southern Asia 2 0.3250 [0.2455; 0.4303] 0.0410 0.2025
## Region = Western Asia 6 1.2017 [0.5323; 2.7130] 1.0357 1.0177
## Region = Northern Africa 4 0.4907 [0.2944; 0.8177] 0.2717 0.5212
## Region = Southern Africa 1 0.2909 [0.2907; 0.2912] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 726003.07 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 1.4666 [ 1.4659; 1.4672] 0.6 1.5 Northern Africa
## ARGENTINA 1.2701 [ 1.2696; 1.2707] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 8.6392 [ 8.6372; 8.6411] 2.1 1.5 Australia and New Zealand
## AUSTRIA 5.5558 [ 5.5533; 5.5584] 0.5 1.5 Western Europe
## BELARUS 0.0745 [ 0.0742; 0.0748] 0.0 1.5 Eastern Europe
## BELGIUM 4.6625 [ 4.6604; 4.6645] 0.5 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.3015 [ 0.3006; 0.3025] 0.0 1.5 Southern Europe
## BRAZIL 0.4401 [ 0.4399; 0.4402] 0.9 1.5 Central and South America and the Caribbean
## BULGARIA 0.9792 [ 0.9780; 0.9804] 0.1 1.5 Eastern Europe
## CANADA 9.9954 [ 9.9937; 9.9971] 3.7 1.5 Northern America
## CHILE 0.7907 [ 0.7900; 0.7914] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0286 [ 0.0286; 0.0286] 0.4 1.5 Eastern Asia
## COLOMBIA 0.1667 [ 0.1665; 0.1669] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 1.0827 [ 1.0810; 1.0843] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 4.9366 [ 4.9344; 4.9388] 0.5 1.5 Eastern Europe
## ECUADOR 0.4980 [ 0.4975; 0.4986] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 1.3411 [ 1.3407; 1.3414] 1.3 1.5 Northern Africa
## ESTONIA 1.7473 [ 1.7435; 1.7510] 0.0 1.5 Northern Europe
## FINLAND 7.1434 [ 7.1397; 7.1470] 0.4 1.5 Northern Europe
## FRANCE 5.6502 [ 5.6492; 5.6511] 3.7 1.5 Western Europe
## GERMANY 5.2710 [ 5.2702; 5.2718] 4.4 1.5 Western Europe
## GREECE 3.9338 [ 3.9318; 3.9358] 0.4 1.5 Southern Europe
## HUNGARY 1.9009 [ 1.8995; 1.9024] 0.2 1.5 Eastern Europe
## INDIA 0.4165 [ 0.4165; 0.4166] 5.6 1.5 Southern Asia
## IRELAND 10.0262 [10.0215; 10.0310] 0.5 1.5 Northern Europe
## ITALY 2.6128 [ 2.6122; 2.6135] 1.6 1.5 Southern Europe
## JAPAN 3.5135 [ 3.5129; 3.5140] 4.6 1.5 Eastern Asia
## JORDAN 0.8999 [ 0.8989; 0.9009] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0727 [ 0.0725; 0.0729] 0.0 1.5 Central Asia
## KUWAIT 1.3133 [ 1.3114; 1.3152] 0.1 1.5 Western Asia
## LATVIA 1.8527 [ 1.8495; 1.8558] 0.0 1.5 Northern Europe
## LEBANON 1.3224 [ 1.3210; 1.3239] 0.1 1.5 Western Asia
## LITHUANIA 1.6490 [ 1.6465; 1.6514] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.3268 [ 4.3179; 4.3357] 0.0 1.5 Western Europe
## MEXICO 0.3396 [ 0.3394; 0.3397] 0.4 1.5 Central and South America and the Caribbean
## MOROCCO 0.1130 [ 0.1129; 0.1132] 0.0 1.5 Northern Africa
## NETHERLANDS 3.7671 [ 3.7656; 3.7687] 0.7 1.5 Western Europe
## NEW ZEALAND 3.4626 [ 3.4598; 3.4654] 0.2 1.5 Australia and New Zealand
## NORWAY 5.8838 [ 5.8804; 5.8872] 0.3 1.5 Northern Europe
## PAKISTAN 0.3136 [ 0.3135; 0.3137] 0.6 1.5 Southern Asia
## PERU 0.2131 [ 0.2128; 0.2134] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1206 [ 0.1205; 0.1207] 0.1 1.5 South-eastern Asia
## POLAND 0.7797 [ 0.7792; 0.7801] 0.3 1.5 Eastern Europe
## PORTUGAL 4.9211 [ 4.9189; 4.9233] 0.5 1.5 Southern Europe
## PUERTO RICO 14.3848 [14.3780; 14.3916] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 1.3004 [ 1.2996; 1.3013] 0.3 1.5 Eastern Europe
## RUSSIA 0.2778 [ 0.2776; 0.2779] 0.4 1.5 Eastern Europe
## SAUDI ARABIA 1.3045 [ 1.3039; 1.3052] 0.4 1.5 Western Asia
## SERBIA 1.1215 [ 1.1203; 1.1226] 0.1 1.5 Southern Europe
## SLOVAKIA 5.0693 [ 5.0662; 5.0724] 0.3 1.5 Eastern Europe
## SLOVENIA 3.6342 [ 3.6299; 3.6385] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.3238 [ 0.3236; 0.3241] 0.2 1.5 Southern Africa
## SOUTH KOREA 2.4374 [ 2.4367; 2.4381] 1.3 1.5 Eastern Asia
## SPAIN 6.8250 [ 6.8237; 6.8262] 3.2 1.5 Southern Europe
## SWEDEN 6.6761 [ 6.6734; 6.6787] 0.7 1.5 Northern Europe
## SWITZERLAND 3.9619 [ 3.9597; 3.9641] 0.3 1.5 Western Europe
## TAIWAN 0.4897 [ 0.4893; 0.4902] 0.1 1.5 Eastern Asia
## THAILAND 0.7037 [ 0.7034; 0.7040] 0.5 1.5 South-eastern Asia
## TUNISIA 0.6241 [ 0.6234; 0.6249] 0.1 1.5 Northern Africa
## TÜRKIYE 5.0237 [ 5.0229; 5.0246] 4.1 1.5 Western Asia
## UNITED ARAB EMIRATES 0.7763 [ 0.7753; 0.7772] 0.1 1.5 Western Asia
## UNITED KINGDOM 12.5535 [12.5521; 12.5549] 8.5 1.5 Northern Europe
## UNITED STATES 12.8246 [12.8239; 12.8252] 42.1 1.5 Northern America
## URUGUAY 1.5550 [ 1.5528; 1.5572] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.6046 [ 0.6041; 0.6051] 0.2 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.9755 [5.9753; 5.9756] 107168.22 0
## Random effects model 1.4404 [1.0592; 1.9588] 2.33 0.0200
##
## Quantifying heterogeneity:
## tau^2 = 1.5987 [1.1852; 3.2346]; tau = 1.2644 [1.0887; 1.7985]
## I^2 = 100.0%; H = 8481.40
##
## Test of heterogeneity:
## Q d.f. p-value
## 4603783703.11 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 12.5692 [12.5686; 12.5698] 7585213.74 100.0%
## Region = Central and South America and t ... 10 0.9315 [ 0.9313; 0.9317] 182365035.33 100.0%
## Region = Northern Europe 8 11.1807 [11.1796; 11.1818] 31775941.84 100.0%
## Region = Eastern Europe 8 1.5317 [ 1.5313; 1.5320] 91982088.94 100.0%
## Region = Southern Europe 8 4.6598 [ 4.6592; 4.6604] 50668219.57 100.0%
## Region = Western Europe 7 5.2185 [ 5.2179; 5.2190] 4608048.38 100.0%
## Region = Australia and New Zealand 2 8.0927 [ 8.0910; 8.0945] 4573784.08 100.0%
## Region = Eastern Asia 4 2.3076 [ 2.3073; 2.3079] 324031018.01 100.0%
## Region = Central Asia 1 0.0727 [ 0.0725; 0.0729] 0.00 --
## Region = South-eastern Asia 2 0.4903 [ 0.4901; 0.4905] 11296697.38 100.0%
## Region = Southern Asia 2 0.4044 [ 0.4044; 0.4045] 1684835.49 100.0%
## Region = Western Asia 6 4.0306 [ 4.0300; 4.0312] 47454229.10 100.0%
## Region = Northern Africa 4 1.2754 [ 1.2751; 1.2757] 10372798.41 100.0%
## Region = Southern Africa 1 0.3238 [ 0.3236; 0.3241] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 3835385792.85 13 0
## Within groups 768397910.26 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 11.3220 [8.8684; 14.4543] 0.0311 0.1762
## Region = Central and South America and t ... 10 0.7170 [0.2941; 1.7478] 2.0669 1.4377
## Region = Northern Europe 8 4.5648 [3.2182; 6.4748] 0.2545 0.5044
## Region = Eastern Europe 8 0.9971 [0.4257; 2.3353] 1.5083 1.2281
## Region = Southern Europe 8 2.1516 [1.4088; 3.2862] 0.3735 0.6112
## Region = Western Europe 7 4.6888 [4.2350; 5.1912] 0.0189 0.1374
## Region = Australia and New Zealand 2 5.4694 [2.2326; 13.3986] 0.4180 0.6465
## Region = Eastern Asia 4 0.5885 [0.1013; 3.4177] 3.2222 1.7951
## Region = Central Asia 1 0.0727 [0.0725; 0.0729] -- --
## Region = South-eastern Asia 2 0.2913 [0.0517; 1.6408] 1.5556 1.2472
## Region = Southern Asia 2 0.3614 [0.2736; 0.4773] 0.0403 0.2007
## Region = Western Asia 6 1.4128 [0.6333; 3.1515] 1.0055 1.0027
## Region = Northern Africa 4 0.6103 [0.3597; 1.0357] 0.2912 0.5397
## Region = Southern Africa 1 0.3238 [0.3236; 0.3241] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 990584.34 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 3.0485 [ 3.0476; 3.0494] 1.2 1.5 Northern Africa
## ARGENTINA 1.3337 [ 1.3331; 1.3342] 0.5 1.5 Central and South America and the Caribbean
## AUSTRALIA 9.3000 [ 9.2980; 9.3020] 2.1 1.5 Australia and New Zealand
## AUSTRIA 5.7414 [ 5.7388; 5.7440] 0.5 1.5 Western Europe
## BELARUS 0.1234 [ 0.1230; 0.1238] 0.0 1.5 Eastern Europe
## BELGIUM 5.2455 [ 5.2433; 5.2477] 0.5 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.3756 [ 0.3746; 0.3767] 0.0 1.5 Southern Europe
## BRAZIL 0.5157 [ 0.5155; 0.5158] 1.0 1.5 Central and South America and the Caribbean
## BULGARIA 1.2179 [ 1.2166; 1.2192] 0.1 1.5 Eastern Europe
## CANADA 10.7934 [10.7916; 10.7951] 3.6 1.5 Northern America
## CHILE 0.8795 [ 0.8788; 0.8802] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0358 [ 0.0358; 0.0358] 0.5 1.5 Eastern Asia
## COLOMBIA 0.1728 [ 0.1727; 0.1730] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 1.2346 [ 1.2328; 1.2364] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 5.6135 [ 5.6112; 5.6159] 0.5 1.5 Eastern Europe
## ECUADOR 0.5272 [ 0.5266; 0.5277] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 2.0563 [ 2.0558; 2.0568] 1.8 1.5 Northern Africa
## ESTONIA 2.2727 [ 2.2684; 2.2769] 0.0 1.5 Northern Europe
## FINLAND 7.7485 [ 7.7446; 7.7523] 0.4 1.5 Northern Europe
## FRANCE 5.7629 [ 5.7619; 5.7639] 3.4 1.5 Western Europe
## GERMANY 5.3977 [ 5.3969; 5.3985] 4.1 1.5 Western Europe
## GREECE 4.2748 [ 4.2727; 4.2768] 0.4 1.5 Southern Europe
## HUNGARY 2.0198 [ 2.0184; 2.0213] 0.2 1.5 Eastern Europe
## INDIA 0.4575 [ 0.4575; 0.4576] 5.6 1.5 Southern Asia
## IRELAND 10.1613 [10.1566; 10.1661] 0.4 1.5 Northern Europe
## ITALY 2.8330 [ 2.8323; 2.8337] 1.6 1.5 Southern Europe
## JAPAN 3.8741 [ 3.8736; 3.8747] 4.5 1.5 Eastern Asia
## JORDAN 1.1300 [ 1.1289; 1.1311] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0892 [ 0.0890; 0.0894] 0.0 1.5 Central Asia
## KUWAIT 2.6181 [ 2.6155; 2.6207] 0.1 1.5 Western Asia
## LATVIA 2.1990 [ 2.1955; 2.2024] 0.0 1.5 Northern Europe
## LEBANON 1.4841 [ 1.4826; 1.4856] 0.1 1.5 Western Asia
## LITHUANIA 1.7040 [ 1.7015; 1.7065] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.4402 [ 4.4314; 4.4491] 0.0 1.5 Western Europe
## MEXICO 0.3813 [ 0.3811; 0.3815] 0.4 1.5 Central and South America and the Caribbean
## MOROCCO 0.1463 [ 0.1461; 0.1465] 0.0 1.5 Northern Africa
## NETHERLANDS 3.8860 [ 3.8844; 3.8875] 0.6 1.5 Western Europe
## NEW ZEALAND 3.8784 [ 3.8754; 3.8813] 0.2 1.5 Australia and New Zealand
## NORWAY 6.3251 [ 6.3215; 6.3286] 0.3 1.5 Northern Europe
## PAKISTAN 0.3403 [ 0.3402; 0.3405] 0.6 1.5 Southern Asia
## PERU 0.2237 [ 0.2234; 0.2239] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1306 [ 0.1305; 0.1307] 0.1 1.5 South-eastern Asia
## POLAND 1.2022 [ 1.2016; 1.2028] 0.4 1.5 Eastern Europe
## PORTUGAL 4.9841 [ 4.9818; 4.9863] 0.5 1.5 Southern Europe
## PUERTO RICO 16.0002 [15.9929; 16.0075] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 1.5396 [ 1.5387; 1.5405] 0.3 1.5 Eastern Europe
## RUSSIA 0.3809 [ 0.3807; 0.3810] 0.5 1.5 Eastern Europe
## SAUDI ARABIA 1.8736 [ 1.8729; 1.8744] 0.6 1.5 Western Asia
## SERBIA 1.4789 [ 1.4775; 1.4802] 0.1 1.5 Southern Europe
## SLOVAKIA 4.7357 [ 4.7327; 4.7387] 0.2 1.5 Eastern Europe
## SLOVENIA 3.8317 [ 3.8273; 3.8361] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.3350 [ 0.3347; 0.3352] 0.2 1.5 Southern Africa
## SOUTH KOREA 2.7612 [ 2.7604; 2.7619] 1.3 1.5 Eastern Asia
## SPAIN 7.1036 [ 7.1024; 7.1049] 3.0 1.5 Southern Europe
## SWEDEN 7.1681 [ 7.1654; 7.1709] 0.6 1.5 Northern Europe
## SWITZERLAND 4.0655 [ 4.0633; 4.0678] 0.3 1.5 Western Europe
## TAIWAN 0.5447 [ 0.5442; 0.5452] 0.1 1.5 Eastern Asia
## THAILAND 0.8093 [ 0.8090; 0.8097] 0.5 1.5 South-eastern Asia
## TUNISIA 0.8396 [ 0.8388; 0.8405] 0.1 1.5 Northern Africa
## TÜRKIYE 5.8508 [ 5.8499; 5.8517] 4.3 1.5 Western Asia
## UNITED ARAB EMIRATES 0.6780 [ 0.6771; 0.6789] 0.1 1.5 Western Asia
## UNITED KINGDOM 13.5562 [13.5548; 13.5577] 8.3 1.5 Northern Europe
## UNITED STATES 14.0011 [14.0005; 14.0018] 41.7 1.5 Northern America
## URUGUAY 1.7113 [ 1.7090; 1.7135] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.4621 [ 0.4617; 0.4625] 0.1 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.4047 [6.4045; 6.4049] 117306.87 0
## Random effects model 1.6515 [1.2170; 2.2411] 3.22 0.0013
##
## Quantifying heterogeneity:
## tau^2 = 1.5771 [1.1376; 3.0811]; tau = 1.2558 [1.0666; 1.7553]
## I^2 = 100.0%; H = 8899.55
##
## Test of heterogeneity:
## Q d.f. p-value
## 5068932381.55 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 13.7122 [13.7116; 13.7129] 9018836.47 100.0%
## Region = Central and South America and t ... 10 0.9997 [ 0.9995; 0.9999] 197505322.21 100.0%
## Region = Northern Europe 8 12.0413 [12.0401; 12.0425] 34575163.07 100.0%
## Region = Eastern Europe 8 1.6558 [ 1.6554; 1.6561] 91869084.78 100.0%
## Region = Southern Europe 8 4.8660 [ 4.8654; 4.8666] 49479430.20 100.0%
## Region = Western Europe 7 5.3659 [ 5.3653; 5.3664] 4314171.43 100.0%
## Region = Australia and New Zealand 2 8.7182 [ 8.7164; 8.7200] 4718592.07 100.0%
## Region = Eastern Asia 4 2.4807 [ 2.4804; 2.4810] 381220077.69 100.0%
## Region = Central Asia 1 0.0892 [ 0.0890; 0.0894] 0.00 --
## Region = South-eastern Asia 2 0.5651 [ 0.5649; 0.5653] 13404327.01 100.0%
## Region = Southern Asia 2 0.4437 [ 0.4437; 0.4438] 2028274.03 100.0%
## Region = Western Asia 6 4.7066 [ 4.7060; 4.7073] 50727290.26 100.0%
## Region = Northern Africa 4 2.2288 [ 2.2284; 2.2292] 22435650.11 100.0%
## Region = Southern Africa 1 0.3350 [ 0.3347; 0.3352] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 4207636162.23 13 0
## Within groups 861296219.31 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 12.2931 [9.5261; 15.8637] 0.0339 0.1840
## Region = Central and South America and t ... 10 0.7541 [0.3090; 1.8403] 2.0721 1.4395
## Region = Northern Europe 8 5.0336 [3.5406; 7.1561] 0.2578 0.5077
## Region = Eastern Europe 8 1.2427 [0.5742; 2.6896] 1.2414 1.1142
## Region = Southern Europe 8 2.4070 [1.6058; 3.6079] 0.3411 0.5841
## Region = Western Europe 7 4.8780 [4.4288; 5.3727] 0.0170 0.1304
## Region = Australia and New Zealand 2 6.0057 [2.5488; 14.1512] 0.3825 0.6184
## Region = Eastern Asia 4 0.6760 [0.1143; 3.9979] 3.2894 1.8137
## Region = Central Asia 1 0.0892 [0.0890; 0.0894] -- --
## Region = South-eastern Asia 2 0.3252 [0.0544; 1.9423] 1.6632 1.2896
## Region = Southern Asia 2 0.3946 [0.2953; 0.5273] 0.0438 0.2092
## Region = Western Asia 6 1.7876 [0.8776; 3.6413] 0.7906 0.8892
## Region = Northern Africa 4 0.9368 [0.5251; 1.6710] 0.3488 0.5906
## Region = Southern Africa 1 0.3350 [0.3347; 0.3352] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 955666.66 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 4
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 2.9435 [ 2.9427; 2.9444] 1.1 1.5 Northern Africa
## ARGENTINA 1.3902 [ 1.3896; 1.3907] 0.5 1.5 Central and South America and the Caribbean
## AUSTRALIA 9.4384 [ 9.4364; 9.4404] 2.0 1.5 Australia and New Zealand
## AUSTRIA 6.0504 [ 6.0478; 6.0531] 0.5 1.5 Western Europe
## BELARUS 0.1868 [ 0.1863; 0.1872] 0.0 1.5 Eastern Europe
## BELGIUM 5.6263 [ 5.6240; 5.6286] 0.6 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.5333 [ 0.5320; 0.5346] 0.0 1.5 Southern Europe
## BRAZIL 0.6318 [ 0.6316; 0.6320] 1.1 1.5 Central and South America and the Caribbean
## BULGARIA 1.3771 [ 1.3757; 1.3785] 0.1 1.5 Eastern Europe
## CANADA 10.8953 [10.8936; 10.8971] 3.5 1.5 Northern America
## CHILE 0.9280 [ 0.9273; 0.9288] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0438 [ 0.0438; 0.0438] 0.5 1.5 Eastern Asia
## COLOMBIA 0.1955 [ 0.1953; 0.1957] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 1.3816 [ 1.3797; 1.3834] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 6.0546 [ 6.0522; 6.0571] 0.6 1.5 Eastern Europe
## ECUADOR 0.5475 [ 0.5469; 0.5481] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 3.9544 [ 3.9538; 3.9551] 3.3 1.5 Northern Africa
## ESTONIA 3.0000 [ 2.9952; 3.0049] 0.0 1.5 Northern Europe
## FINLAND 8.2826 [ 8.2786; 8.2865] 0.4 1.5 Northern Europe
## FRANCE 6.0179 [ 6.0169; 6.0188] 3.4 1.5 Western Europe
## GERMANY 5.6636 [ 5.6628; 5.6645] 4.0 1.5 Western Europe
## GREECE 4.6340 [ 4.6318; 4.6361] 0.4 1.5 Southern Europe
## HUNGARY 2.1507 [ 2.1492; 2.1522] 0.2 1.5 Eastern Europe
## INDIA 0.5129 [ 0.5129; 0.5130] 6.0 1.5 Southern Asia
## IRELAND 8.5541 [ 8.5498; 8.5584] 0.4 1.5 Northern Europe
## ITALY 2.8818 [ 2.8811; 2.8825] 1.5 1.5 Southern Europe
## JAPAN 4.1321 [ 4.1315; 4.1327] 4.5 1.5 Eastern Asia
## JORDAN 0.6513 [ 0.6504; 0.6521] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.1047 [ 0.1044; 0.1049] 0.0 1.5 Central Asia
## KUWAIT 3.2250 [ 3.2222; 3.2279] 0.1 1.5 Western Asia
## LATVIA 2.6394 [ 2.6357; 2.6432] 0.0 1.5 Northern Europe
## LEBANON 1.5687 [ 1.5672; 1.5703] 0.1 1.5 Western Asia
## LITHUANIA 2.0118 [ 2.0091; 2.0146] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.4238 [ 4.4151; 4.4326] 0.0 1.5 Western Europe
## MEXICO 0.4126 [ 0.4124; 0.4128] 0.4 1.5 Central and South America and the Caribbean
## MOROCCO 0.2011 [ 0.2009; 0.2014] 0.1 1.5 Northern Africa
## NETHERLANDS 4.0538 [ 4.0522; 4.0554] 0.6 1.5 Western Europe
## NEW ZEALAND 5.0261 [ 5.0228; 5.0295] 0.2 1.5 Australia and New Zealand
## NORWAY 6.6320 [ 6.6284; 6.6356] 0.3 1.5 Northern Europe
## PAKISTAN 0.3993 [ 0.3992; 0.3995] 0.7 1.5 Southern Asia
## PERU 0.2562 [ 0.2559; 0.2565] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1574 [ 0.1573; 0.1576] 0.1 1.5 South-eastern Asia
## POLAND 1.6797 [ 1.6790; 1.6803] 0.5 1.5 Eastern Europe
## PORTUGAL 5.1522 [ 5.1499; 5.1545] 0.5 1.5 Southern Europe
## PUERTO RICO 15.1233 [15.1160; 15.1305] 0.4 1.5 Central and South America and the Caribbean
## ROMANIA 1.6365 [ 1.6356; 1.6375] 0.3 1.5 Eastern Europe
## RUSSIA 0.5050 [ 0.5048; 0.5052] 0.6 1.5 Eastern Europe
## SAUDI ARABIA 1.1357 [ 1.1351; 1.1363] 0.3 1.5 Western Asia
## SERBIA 1.9559 [ 1.9543; 1.9574] 0.1 1.5 Southern Europe
## SLOVAKIA 5.1626 [ 5.1594; 5.1657] 0.2 1.5 Eastern Europe
## SLOVENIA 3.9999 [ 3.9954; 4.0044] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.3738 [ 0.3736; 0.3741] 0.2 1.5 Southern Africa
## SOUTH KOREA 3.0995 [ 3.0987; 3.1003] 1.4 1.5 Eastern Asia
## SPAIN 7.3282 [ 7.3269; 7.3295] 2.9 1.5 Southern Europe
## SWEDEN 7.6667 [ 7.6638; 7.6695] 0.7 1.5 Northern Europe
## SWITZERLAND 4.2259 [ 4.2237; 4.2282] 0.3 1.5 Western Europe
## TAIWAN 0.6414 [ 0.6408; 0.6419] 0.1 1.5 Eastern Asia
## THAILAND 0.9387 [ 0.9383; 0.9390] 0.6 1.5 South-eastern Asia
## TUNISIA 2.0328 [ 2.0314; 2.0342] 0.2 1.5 Northern Africa
## TÜRKIYE 6.0983 [ 6.0974; 6.0992] 4.3 1.5 Western Asia
## UNITED ARAB EMIRATES 0.6640 [ 0.6631; 0.6648] 0.1 1.5 Western Asia
## UNITED KINGDOM 13.8879 [13.8864; 13.8894] 8.0 1.5 Northern Europe
## UNITED STATES 14.2539 [14.2532; 14.2546] 40.1 1.5 Northern America
## URUGUAY 1.5402 [ 1.5380; 1.5424] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.4182 [ 0.4178; 0.4186] 0.1 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.4210 [6.4208; 6.4212] 121182.01 0
## Random effects model 1.8260 [1.3537; 2.4631] 3.94 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.5159 [1.0781; 2.8964]; tau = 1.2312 [1.0383; 1.7019]
## I^2 = 100.0%; H = 9069.14
##
## Test of heterogeneity:
## Q d.f. p-value
## 5263957332.51 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 13.9518 [13.9512; 13.9524] 9802714.94 100.0%
## Region = Central and South America and t ... 10 0.9891 [ 0.9890; 0.9893] 171390798.83 100.0%
## Region = Northern Europe 8 12.2841 [12.2829; 12.2853] 34829258.32 100.0%
## Region = Eastern Europe 8 1.8254 [ 1.8251; 1.8258] 93547022.86 100.0%
## Region = Southern Europe 8 5.0234 [ 5.0228; 5.0241] 50160342.70 100.0%
## Region = Western Europe 7 5.6248 [ 5.6243; 5.6254] 4606414.77 100.0%
## Region = Australia and New Zealand 2 8.9062 [ 8.9045; 8.9080] 3139085.02 100.0%
## Region = Eastern Asia 4 2.5824 [ 2.5821; 2.5827] 434629834.21 100.0%
## Region = Central Asia 1 0.1047 [ 0.1044; 0.1049] 0.00 --
## Region = South-eastern Asia 2 0.6511 [ 0.6509; 0.6513] 15544990.16 100.0%
## Region = Southern Asia 2 0.4992 [ 0.4991; 0.4992] 1729151.68 100.0%
## Region = Western Asia 6 4.9872 [ 4.9865; 4.9878] 63531077.31 100.0%
## Region = Northern Africa 4 3.4522 [ 3.4517; 3.4527] 27574420.39 100.0%
## Region = Southern Africa 1 0.3738 [ 0.3736; 0.3741] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 4353472221.33 13 0
## Within groups 910485111.19 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 12.4620 [9.5770; 16.2160] 0.0361 0.1900
## Region = Central and South America and t ... 10 0.7859 [0.3514; 1.7577] 1.6867 1.2987
## Region = Northern Europe 8 5.4662 [3.8589; 7.7429] 0.2525 0.5025
## Region = Eastern Europe 8 1.4878 [0.7272; 3.0440] 1.0673 1.0331
## Region = Southern Europe 8 2.7097 [1.8201; 4.0340] 0.3298 0.5743
## Region = Western Europe 7 5.0852 [4.6139; 5.6046] 0.0172 0.1313
## Region = Australia and New Zealand 2 6.8876 [3.7143; 12.7719] 0.1985 0.4456
## Region = Eastern Asia 4 0.7744 [0.1307; 4.5895] 3.2972 1.8158
## Region = Central Asia 1 0.1047 [0.1044; 0.1049] -- --
## Region = South-eastern Asia 2 0.3844 [0.0668; 2.2115] 1.5939 1.2625
## Region = Southern Asia 2 0.4526 [0.3541; 0.5784] 0.0313 0.1770
## Region = Western Asia 6 1.5730 [0.6396; 3.8685] 1.2648 1.1246
## Region = Northern Africa 4 1.4770 [0.8489; 2.5696] 0.3193 0.5651
## Region = Southern Africa 1 0.3738 [0.3736; 0.3741] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1046730.16 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
#Multinational
gaba_global2<-as.data.frame(do.call(rbind, datout))[c(5:9)]
gaba_global2$Year<-as.numeric(gaba_global2$Year)
gaba_global2$`DDD/TID`<-as.numeric(gaba_global2$`DDD/TID`)
gaba_global2$`DDD/TID - lower`<-as.numeric(gaba_global2$`DDD/TID - lower`)
gaba_global2$`DDD/TID - upper`<-as.numeric(gaba_global2$`DDD/TID - upper`)
gaba_global2$Area<-"Multinational"
gaba_global2$Drug<-c(rep(("Gabapentin"), 11),
rep(("Gabapentin Enacarbil"), 11),
rep(("Pregabalin"), 11),
rep(("Gabapentinoids"), 11))
gaba_global3<-subset(gaba_global2,Drug!="MIROGABALIN")
a<-ggplot(gaba_global3, aes(x = Year, y = `DDD/TID`*10, group=Drug, colour=Drug, fill=Drug))+
geom_line() +
geom_ribbon(aes(ymin = `DDD/TID - lower`*10, ymax = `DDD/TID - upper`*10), alpha = 0.1, colour = NA) +
scale_x_continuous(breaks = c(2008:2018))+
ggtitle("Pooled multinational gabapentinoids consumption over time")+
ylab("Defined daily dose per 10000 inhabitants per day")+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
a
ggsave(filename="Figure_2_pooled_by year drug.png", plot=a, height =7, width=11,device="png",
path="D:/R/midas gaba/R1/figures",
dpi=500)
##Regional
gaba_regional<-as.data.frame(do.call(rbind, datout))[c(14:18)]
gaba_regional$Year<-as.numeric(gaba_regional$Year)
out <- unlist(gaba_regional)
Year<-out[1:44]
Drug<-out[45:88]
DDDTID<-out[89:704]
lower<-out[705:1320]
upper<-out[1321:1936]
out2<-data.frame(Year,Drug)
out3<-do.call("rbind", replicate(14, out2, simplify = FALSE))
newdata <- out3%>% arrange(Drug, Year)
newdata$DDDTID<-DDDTID
newdata$lower<-lower
newdata$upper<-upper
region_name<-(rep(c("Northern America","Central and South America and the Caribbean",
"Northern Europe", "Eastern Europe","Southern Europe", "Western Europe",
"Australia and New Zealand" ,
"Eastern Asia" , "Central Asia",
"South-eastern Asia","Southern Asia" ,
"Western Asia",
"Northern Africa","Southern Africa"),44))
newdata$Area<-region_name
newdata$Drug<-c(rep(("Gabapentin"), 154),
rep(("Gabapentin Enacarbil"), 154),
rep(("Pregabalin"), 154),
rep(("Gabapentinoids"), 154))
gaba_regional2<-newdata
gaba_regional2$`DDD/TID`<-gaba_regional2$DDDTID
gaba_regional2$`DDD/TID - lower`<-gaba_regional2$lower
gaba_regional2$`DDD/TID - upper`<-gaba_regional2$upper
gaba_regional3<-gaba_regional2[c(1,2,6:9)]
pool_Global_region<-rbind(gaba_global3,gaba_regional3)
pool_Global_region$Drug<- factor(pool_Global_region$Drug, levels = c("Gabapentinoids","Gabapentin","Gabapentin Enacarbil","Pregabalin"))
pool_Global_region$Area<-factor(pool_Global_region$Area,levels=c("Multinational","Northern America","Central and South America and the Caribbean",
"Northern Europe", "Eastern Europe","Southern Europe", "Western Europe",
"Australia and New Zealand" ,
"Eastern Asia" , "Central Asia",
"South-eastern Asia","Southern Asia" ,
"Western Asia",
"Northern Africa","Southern Africa"))
pool_Global_region$Year<-as.numeric(pool_Global_region$Year)
pool_Global_region[is.na(pool_Global_region)]<-0
library(ggbreak)
a<-ggplot(pool_Global_region, aes(x = Year, y = `DDD/TID`, group=Drug, colour=Drug, fill=Drug))+
geom_line() +
facet_wrap(.~Area,nrow=1)+
geom_ribbon(aes(ymin = `DDD/TID - lower`, ymax = `DDD/TID - upper`), alpha = 0.1, colour = NA) +
scale_y_break(c(20,62),scales = 0.1)+
theme_classic()+
scale_x_continuous(breaks = c(2008:2018))+
theme(panel.border=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.line=element_line(),axis.ticks.x = element_blank())+
ggtitle("Pooled multinational gabapentinoids consumption over time")
a
ggsave(filename="Reference_1_pooled_by Region year drug.png", plot=a, height =7, width=40,device="png",
path="D:/R/midas gaba/R1/figures",
dpi=500)
write.csv(pool_Global_region,"D:/R/midas gaba/R1/Output_3_Pooled_Gaba_consumption_CI.csv")
library(DT)
datatable(pool_Global_region[c(6,1:5)], options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Visualisation: Geographical distribution of gabapentinoids use for 2008 and 2018
2008
library(ggplot2)
library(dplyr)
library(maps)
library(viridis)
theme_set(
theme_grey()
)
library(mapproj)
map.1<-subset(pool.new_set.2, Year==2008 & Drug=="Gabapentinoids")
world_map <- map_data("world")
# ggplot(world_map, aes(x = long, y = lat, group = group)) +
# geom_polygon(fill="lightgray", colour = "white")
world_map.2 <-world_map
setDF(world_map.2)
world_map.2$region <- toupper(world_map.2$region)
# map.1$Country %in% world_map.2$region
# sort(unique(world_map.2$region))
world_map.2$region[world_map.2$region == "UK"] <- "UNITED KINGDOM"
world_map.2$region[world_map.2$region == "USA"] <- "UNITED STATES"
world_map.2$region[world_map.2$region == "TURKEY"] <- "TÜRKİYE"
names(map.1)[names(map.1) == "Country"] <- "region"
map.2 <- right_join(map.1, world_map.2)
#change to categorical
map.2015 <- map.2 %>% mutate(category=cut(DDDPTPD*10,
breaks=c(-Inf,
0.01,
0.1,
1,
10,50,
100,
200,
Inf),
labels=c("0-0.01",
">0.01-0.1",
">0.1-1",
">1-10",
">10-50",
">50-100",
">100-200",
">200")))
y<-ggplot(map.2015, group = group)+
geom_map(map = map.2015, color = "black",
aes(map_id = region, fill = category, group = group))+
expand_limits(x = map.2015$long, y = map.2015$lat)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
legend.position = "right",
panel.background = element_rect(fill = 'white'))+
guides(fill=guide_legend(title="DDD/TID"))+
scale_fill_viridis(
option="turbo",drop=FALSE,
name = "Defined Daily Dose per capita per day",
discrete = T,
direction = +1,
guide = guide_legend(
keyheight = unit(5, units = "mm"),
title.position = 'top',
reverse = FALSE
))+
ggtitle("2008")
y
ggsave(filename="Figure 3a_map 2008_gaba.png", plot=y, height =7, width=11,device="png",
path="D:/R/midas gaba/R1/figures",
dpi=500)
2018
map.1<-map.1<-subset(pool.new_set.2, Year==2018 & Drug=="Gabapentinoids")
world_map <- map_data("world")
# ggplot(world_map, aes(x = long, y = lat, group = group)) +
# geom_polygon(fill="lightgray", colour = "white")
world_map.2 <-world_map
setDF(world_map.2)
world_map.2$region <- toupper(world_map.2$region)
# map.1$Country %in% world_map.2$region
# sort(unique(world_map.2$region))
world_map.2$region[world_map.2$region == "UK"] <- "UNITED KINGDOM"
world_map.2$region[world_map.2$region == "USA"] <- "UNITED STATES"
world_map.2$region[world_map.2$region == "TURKEY"] <- "TÜRKİYE"
names(map.1)[names(map.1) == "Country"] <- "region"
map.2 <- right_join(map.1, world_map.2)
#change to categorical
map.2019 <- map.2 %>% mutate(category=cut(DDDPTPD*10,
breaks=c(-Inf,
0.01,
0.1,
1,
10,50,
100,
200,
Inf),
labels=c("0-0.01",
">0.01-0.1",
">0.1-1",
">1-10",
">10-50",
">50-100",
">100-200",
">200")))
y<-ggplot(map.2019, group = group)+
geom_map(map = map.2019, color = "black",
aes(map_id = region, fill = category, group = group))+
expand_limits(x = map.2019$long, y = map.2019$lat)+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
legend.position = "right",
panel.background = element_rect(fill = 'white'))+
scale_fill_viridis(
option="turbo", drop=FALSE,
name = "Defined Daily Dose per capita per day",
discrete = T,
direction = +1,
guide = guide_legend(title="DDD/TID",
keyheight = unit(5, units = "mm"),
title.position = 'top',
reverse = FALSE
))+
ggtitle("2018")
y
ggsave(filename="Figure 3b_map 2018_gaba.png", plot=y, height =7, width=11,device="png",
path="D:/R/midas gaba/R1/figures",
dpi=500)
Calculate percentage of regions
##National
library(dplyr)
region_percent_gaba<-pool.new_set.2 %>% filter(Year==2018 & Drug=="Gabapentinoids")
region_percent_gaba$percent<-region_percent_gaba$DDDPTPD/sum(region_percent_gaba$DDDPTPD)
library(DT)
datatable(region_percent_gaba[,c(6,3,15)], caption="Gabapentinoids", options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
region_percent_gaba<-pool.new_set.2 %>% filter(Year==2018 & Drug=="Gabapentinoids")
region_percent_gaba$percent<-region_percent_gaba$DDDPTPD/sum(region_percent_gaba$DDDPTPD)
##Regional
region_percent_gaba[c(7,8)] %>%
dplyr::group_by(Region) %>% dplyr::mutate(sumDDDPTPD = sum(region_percent_gaba$DDDPTPD))
library(data.table)
setDT(region_percent_gaba) # set the data frame as data table
as<-region_percent_gaba[, list(sumDDDPTPD = sum(DDDPTPD, na.rm=F)),
by=list(Region)]
region_2018<-as %>%mutate(percent = prop.table(sumDDDPTPD))
datatable(region_2018, caption="Gabapentinoids", options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Average annual percentage change
Data entries with DDD=0 were removed as they do not contribute to average annual percentage change where measures changes between study years. 2106 observations after removing DDD=0
Data view - showing which zeros were removed
National level
options(width = 30)
library(plyr)
library(Hmisc)
library(DT)
library(tibble)
lm.cty_gaba_2<-subset(lm.cty_gaba, DDD>0)
models <- dlply(lm.cty_gaba_2, .(Country,Drug), function(df)
lm(log(DDDPTPD) ~ Year, data = df))
coef<-sapply(models, function(df) summary(df)$coefficients[2])
lm.results<-data.frame(coef)
ad_extract_ci <- function(x){
temp_lw <- confint.lm(x)[2,1]
temp_up <- confint.lm(x)[2,2]
return(c(temp_lw,temp_up))
}
lower<-unlist(lapply(lapply(models,ad_extract_ci),"[",1))
lm.results<-cbind(lm.results, lower)
upper<-unlist(lapply(lapply(models,ad_extract_ci),"[",2))
lm.results<-cbind(lm.results, upper)
pvalue<-sapply(models, function(df) summary(df)$coefficients[8])
lm.results<-cbind(lm.results, pvalue)
lm.results<-rownames_to_column(lm.results)
colnames(lm.results)[which(names(lm.results) == "rowname")] <- "Country"
lm.results$expcoef<-(exp(lm.results$coef)-1)*100
lm.results$explower<-(exp(lm.results$lower)-1)*100
lm.results$expupper<-(exp(lm.results$upper)-1)*100
lm.results2<-(lm.results) %>% separate(Country, c("Country", "Drug"), sep="[.]")
cty.lm.results<-distinct(left_join(lm.results2,pool.new_set.2[c(2,3,7)]))
Global
Global_model_1 is a two level (individual countries) random intercepts model with AR1.
Examples:
#random intercept fitRandomIntercept <- lme(value “~” time,random= “~” 1|subject,data=repeatdatax)
#random intercept and slope fitRandomInterceptSlope <- lme(value“"time,random="”1+time|subject,data=repeatdatax)
Note to self: 1. The lme function of nlme is used instead of lme4 as handling autocorrelation is more straightforward with nlme; specfically, “in nlme, it is possible to specify the variance-covariance matrix for the random effects (e.g. an AR(1)); it is not possible in lme4.”. Read: https://stats.stackexchange.com/questions/5344/how-to-choose-nlme-or-lme4-r-library-for-mixed-effects-models
Intercept 1+ is included implicitly.
The results of Global_model_1 can be reproduced by the follwo SAS codes:
proc sort data=ana.globe; by Country year;run;
proc glimmix data=ana.globe
plots=residualpanel(conditional marginal);;
class Country yearn ;
model lnDDDPTPD = year /solution cl ;
random intercept /subject=Country;
random yearn/subject=Country type=AR(1) residual;
*random residual /subject=Country type=ar(1) ;
ods output ParameterEstimates = _es_globe;
run;
quit;
data ana.global_est_gaba;set _es_globe;
if effect=‘Year’;
DDDPTPD_change=(exp (Estimate)-1)100;
change_lcl=(exp (Lower)-1)100;
change_ucl=(exp (Upper)-1)*100;
run;
library(plyr)
library(lme4)
library(nlme)
#
# mixed_results_Global<-data.frame()
# lm.cty_gaba_x<-split(lm.cty_gaba_2, list(lm.cty_gaba_2$Drug))
# for (i in 1:4){
# Global_model_1 <- lme(log(DDDPTPD)~Year, random = ~1|Country,
# correlation = corAR1(form = ~ Year|Country), data = as.data.frame(lm.cty_gaba_x[i], col.names = c("")))
# summary(Global_model_1)
# mixed_est<-intervals(Global_model_1, level = 0.95, which = "fixed")
# mixed_est_2 <- data.frame(mixed_est$fixed)
# mixed_pval<-anova(Global_model_1)
# mixed_pval<-as.data.frame(mixed_pval)
# mixed_results_1<-cbind(mixed_est_2,mixed_pval)
# mixed_results_1$Model<-"Global_country_int_wAR1"
# mixed_results_1$expcoef<-(exp(mixed_results_1$est.)-1)*100
# mixed_results_1$explower<-(exp(mixed_results_1$lower)-1)*100
# mixed_results_1$expupper<-(exp(mixed_results_1$upper)-1)*100
#
# mixglo<-rbind(mixed_results_1)
# mixglo$Drug<-c(unique(lm.cty_gaba$Drug)[i])
# mixed_results_Global<-rbind(mixed_results_Global,mixglo)
# }
#
# datatable(mixed_results_Global[c(8:12,7,1:6)], options = list(
# autoWidth = TRUE,
# columnDefs = list(list(width = '100px', targets = c(1, 3)))
# ))
asd<-subset(lm.cty_gaba_2, Drug=="Gabapentinoids")
Global_model_1 <- lme(log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(form = ~ Year|Country), data = asd)
mixed_est<-intervals(Global_model_1, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(Global_model_1)
mixed_pval<-as.data.frame(mixed_pval)
mixed_results_1<-cbind(mixed_est_2,mixed_pval)
mixed_results_1$Drug<-"Gabapentinoids"
mixed_results_1$expcoef<-(exp(mixed_results_1$est.)-1)*100
mixed_results_1$explower<-(exp(mixed_results_1$lower)-1)*100
mixed_results_1$expupper<-(exp(mixed_results_1$upper)-1)*100
asd<-subset(lm.cty_gaba_2, Drug=="Gabapentin")
Global_model_1 <- lme(log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(form = ~ Year|Country), data = asd)
mixed_est<-intervals(Global_model_1, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(Global_model_1)
mixed_pval<-as.data.frame(mixed_pval)
mixed_results_2<-cbind(mixed_est_2,mixed_pval)
mixed_results_2$Drug<-"Gabapentin"
mixed_results_2$expcoef<-(exp(mixed_results_2$est.)-1)*100
mixed_results_2$explower<-(exp(mixed_results_2$lower)-1)*100
mixed_results_2$expupper<-(exp(mixed_results_2$upper)-1)*100
asd<-subset(lm.cty_gaba_2, Drug=="Pregabalin")
Global_model_1 <- lme(log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(form = ~ Year|Country), data = asd)
mixed_est<-intervals(Global_model_1, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(Global_model_1)
mixed_pval<-as.data.frame(mixed_pval)
mixed_results_3<-cbind(mixed_est_2,mixed_pval)
mixed_results_3$Drug<-"Pregabalin"
mixed_results_3$expcoef<-(exp(mixed_results_3$est.)-1)*100
mixed_results_3$explower<-(exp(mixed_results_3$lower)-1)*100
mixed_results_3$expupper<-(exp(mixed_results_3$upper)-1)*100
asd<-subset(lm.cty_gaba_2, Drug=="Gabapentin Enacarbil")
Global_model_1 <- lme(log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(form = ~ Year|Country), data = asd)
mixed_est<-intervals(Global_model_1, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(Global_model_1)
mixed_pval<-as.data.frame(mixed_pval)
mixed_results_4<-cbind(mixed_est_2,mixed_pval)
mixed_results_4$Drug<-"Gabapentin Enacarbil"
mixed_results_4$expcoef<-(exp(mixed_results_4$est.)-1)*100
mixed_results_4$explower<-(exp(mixed_results_4$lower)-1)*100
mixed_results_4$expupper<-(exp(mixed_results_4$upper)-1)*100
mixed_results_Global<-rbind(mixed_results_1,mixed_results_2,mixed_results_3,mixed_results_4)
datatable(mixed_results_Global, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Regional
Gabapentinoids
library(DT)
library(data.table)
regionalmlm<-subset(lm.cty_gaba_2,Drug=="Gabapentinoids")
my_update_function <- function(x){
regionalmlm2<-regionalmlm[regionalmlm$Region==x,]
regional_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = regionalmlm2)
mixed_est<-intervals(regional_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(regional_lme)
mixed_results_regional<-cbind(mixed_est_2,mixed_pval)
mixed_results_regional$Model<-x
mixed_results_regional$expcoef<-(exp(mixed_results_regional$est.)-1)*100
mixed_results_regional$explower<-(exp(mixed_results_regional$lower)-1)*100
mixed_results_regional$expupper<-(exp(mixed_results_regional$upper)-1)*100
mixed_results_regional <- mixed_results_regional[2,]
return(mixed_results_regional)
}
lapply(unique(lm.cty_gaba_2$Region),my_update_function)
gabapentinoids.mlm_region<-rbindlist(lapply(unique(lm.cty_gaba_2$Region),
my_update_function))
gabapentinoids.mlm_region<-as.data.frame(gabapentinoids.mlm_region)
gabapentinoids.mlm_region$Drug<-"Gabapentinoids"
Gabapentin
library(DT)
library(data.table)
regionalmlm<-subset(lm.cty_gaba_2,Drug=="Gabapentin")
my_update_function <- function(x){
regionalmlm2<-regionalmlm[regionalmlm$Region==x,]
regional_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = regionalmlm2)
mixed_est<-intervals(regional_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(regional_lme)
mixed_results_regional<-cbind(mixed_est_2,mixed_pval)
mixed_results_regional$Model<-x
mixed_results_regional$expcoef<-(exp(mixed_results_regional$est.)-1)*100
mixed_results_regional$explower<-(exp(mixed_results_regional$lower)-1)*100
mixed_results_regional$expupper<-(exp(mixed_results_regional$upper)-1)*100
mixed_results_regional <- mixed_results_regional[2,]
return(mixed_results_regional)
}
lapply(unique(regionalmlm$Region),my_update_function)
gabapentin.mlm_region<-rbindlist(lapply(unique(regionalmlm$Region),
my_update_function))
gabapentin.mlm_region<-as.data.frame(gabapentin.mlm_region)
gabapentin.mlm_region$Drug<-"Gabapentin"
Gabapentin enacarbil
library(DT)
library(data.table)
regionalmlm<-subset(lm.cty_gaba_2,Drug=="Gabapentin Enacarbil")
my_update_function <- function(x){
regionalmlm2<-regionalmlm[regionalmlm$Region==x,]
regional_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = regionalmlm2)
mixed_est<-intervals(regional_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(regional_lme)
mixed_results_regional<-cbind(mixed_est_2,mixed_pval)
mixed_results_regional$Model<-x
mixed_results_regional$expcoef<-(exp(mixed_results_regional$est.)-1)*100
mixed_results_regional$explower<-(exp(mixed_results_regional$lower)-1)*100
mixed_results_regional$expupper<-(exp(mixed_results_regional$upper)-1)*100
mixed_results_regional <- mixed_results_regional[2,]
return(mixed_results_regional)
}
lapply(unique(regionalmlm$Region),my_update_function)
enacarbil.mlm_region<-rbindlist(lapply(unique(regionalmlm$Region),
my_update_function))
enacarbil.mlm_region<-as.data.frame(enacarbil.mlm_region)
enacarbil.mlm_region$Drug<-"Gabapentin Enacarbil"
Pregabalin
library(DT)
library(data.table)
regionalmlm<-subset(lm.cty_gaba_2,Drug=="Pregabalin")
my_update_function <- function(x){
regionalmlm2<-regionalmlm[regionalmlm$Region==x,]
regional_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = regionalmlm2)
mixed_est<-intervals(regional_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(regional_lme)
mixed_results_regional<-cbind(mixed_est_2,mixed_pval)
mixed_results_regional$Model<-x
mixed_results_regional$expcoef<-(exp(mixed_results_regional$est.)-1)*100
mixed_results_regional$explower<-(exp(mixed_results_regional$lower)-1)*100
mixed_results_regional$expupper<-(exp(mixed_results_regional$upper)-1)*100
mixed_results_regional <- mixed_results_regional[2,]
return(mixed_results_regional)
}
lapply(unique(regionalmlm$Region),my_update_function)
pregabalin.mlm_region<-rbindlist(lapply(unique(regionalmlm$Region),
my_update_function))
pregabalin.mlm_region<-as.data.frame(pregabalin.mlm_region)
pregabalin.mlm_region$Drug<-"Pregabalin"
region_reg_mlm<-rbind(gabapentinoids.mlm_region,gabapentin.mlm_region, enacarbil.mlm_region, pregabalin.mlm_region)
Result table
bind_cty_lm<-cty.lm.results[c(2,10,1,7:9,6)]
names(bind_cty_lm)[7]<-"p-value"
bind_reg_lm<-region_reg_mlm
bind_reg_lm$Country<-bind_reg_lm$Model
bind_reg_lm$Region<-bind_reg_lm$Model
bind_reg_lm<-bind_reg_lm[c(12,14,13,9:11,7)]
bind_glo_lm<-mixed_results_Global[c(2,4,6,8),]
bind_glo_lm<-bind_glo_lm[c(8:11,7)]
bind_glo_lm$Country<-"Multinational"
bind_glo_lm$Region<-"Multinational"
bind_glo_lm<-bind_glo_lm[c(7,6,1:5)]
bind_lm<-rbind(bind_cty_lm,bind_reg_lm,bind_glo_lm)
write.csv(bind_lm,"D:/R/midas gaba/R1/Output_4_lm_cty_region_multi.csv")
datatable(bind_lm, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Visualisation: Average annual percentage chnage of gabapantinoids - Multinational, Regional, National
library(dplyr)
library(ggplot2)
bind_lm$Country<- factor(bind_lm$Country, levels = c("Multinational",
"Northern America",
"CANADA",
"UNITED STATES",
"Central and South America and the Caribbean",
"ARGENTINA",
"BRAZIL",
"CHILE",
"COLOMBIA",
"ECUADOR",
"MEXICO",
"PERU",
"PUERTO RICO",
"URUGUAY",
"VENEZUELA",
"Northern Europe",
"ESTONIA",
"FINLAND",
"IRELAND",
"LATVIA",
"LITHUANIA",
"NORWAY",
"SWEDEN",
"UNITED KINGDOM",
"Eastern Europe",
"BELARUS",
"BULGARIA",
"CZECH REPUBLIC",
"HUNGARY",
"POLAND",
"ROMANIA",
"RUSSIA",
"SLOVAKIA",
"Southern Europe",
"BOSNIA AND HERZEGOVINA",
"CROATIA",
"GREECE",
"ITALY",
"PORTUGAL",
"SERBIA",
"SLOVENIA",
"SPAIN",
"Western Europe",
"AUSTRIA",
"BELGIUM",
"FRANCE",
"GERMANY",
"LUXEMBOURG",
"NETHERLANDS",
"SWITZERLAND",
"Australia and New Zealand",
"AUSTRALIA",
"NEW ZEALAND",
"Eastern Asia",
"CHINA",
"JAPAN",
"SOUTH KOREA",
"TAIWAN",
"Central Asia",
"KAZAKHSTAN",
"South-eastern Asia",
"PHILIPPINES",
"THAILAND",
"INDIA",
"PAKISTAN",
"Southern Asia",
"JORDAN",
"KUWAIT",
"LEBANON",
"SAUDI ARABIA",
"TÜRKİYE",
"UNITED ARAB EMIRATES",
"Western Asia",
"ALGERIA",
"EGYPT",
"MOROCCO",
"Northern Africa",
"TUNISIA",
"Southern Africa",
"SOUTH AFRICA"
))
bind_lm$Region<-factor(bind_lm$Region,levels=c("Multinational","Northern America","Central and South America and the Caribbean",
"Northern Europe", "Eastern Europe","Southern Europe", "Western Europe",
"Australia and New Zealand" ,
"Eastern Asia" , "Central Asia",
"South-eastern Asia","Southern Asia" ,
"Western Asia",
"Northern Africa","Southern Africa"))
bind_lm$Drug<-factor(bind_lm$Drug,levels=c("Gabapentinoids","Gabapentin","Gabapentin Enacarbil","Pregabalin"))
a<-ggplot(data = bind_lm, aes(x = reorder(Country, desc(Country)),y = expcoef,fill=Region)) + #don't bother plotting the NA
geom_bar(stat="identity")+
geom_errorbar(
aes(x=`Country`,
ymin = explower,
ymax = expupper),
color = "red"
)+
scale_y_continuous(breaks = seq(-100, 3500,10), limits = c(-100, 3480))+
facet_wrap(~Drug,nrow = 1)+coord_flip()+
facet_grid(rows = vars(Drug),
scales = "free_y", switch = "y",
space = "free_y",
labeller = label_wrap_gen(width=20)) +
theme(axis.text.x.top= element_blank(),axis.ticks.x.top= element_blank(),
axis.text.x = element_text(angle = 45, size = 8, vjust = 0.5, hjust=0),
legend.position = "right")+
xlab("Geographical location")+
ylab("Average annual percentage change, %")
pg <- a +
scale_y_break(c(140, 420), scales=0.1)+
scale_y_break(c(440,3460), scales=0.1)
pg
ggsave(filename="Reference 2_AAPC.png", plot=pg, height =30, width=12,device="png",
path="D:/R/midas gaba/R1/figures",
dpi=500)
bind_lm_gaba<-subset(bind_lm,Drug=="Gabapentinoids")
a<-ggplot(data = bind_lm_gaba, aes(x = reorder(Country, desc(Country)),y = expcoef,fill=Region)) + #don't bother plotting the NA
geom_bar(stat="identity")+
geom_errorbar(
aes(x=`Country`,
ymin = explower,
ymax = expupper),
color = "red"
)+
scale_y_continuous(breaks = seq(-100, 140,10))+
coord_flip()+
# facet_grid(rows = vars(Region), cols = vars(Drug),
# scales = "free_y", switch = "y",
# space = "free_y",
# labeller = label_wrap_gen(width=20)) +
theme(axis.text.x.top= element_blank(),axis.ticks.x.top= element_blank(),
axis.text.x = element_text(angle = 45, size = 8, vjust = 0.5, hjust=0),
legend.position = "right")+
xlab("Geographical location")+
ylab("Average annual percentage change, %")
a
# pg <- a +
# scale_y_break(c(420, 3400), scales=1)
# pg
# geom_errorbar(
# aes(x=`Country`,
# ymin = explower,
# ymax = expupper),
# color = "red"
# )
ggsave(filename="Figure 4_AAPC_gabapentinoid.png", plot=a, height =12, width=10,device="png",
path="D:/R/midas gaba/R1/figures",
dpi=500)
Subgroup: country income level
Gabapentinoids
library(readxl)
GDP <- read_excel("D:/R/midas gaba/Income.xlsx")
'%ni%' <- Negate('%in%')
GDP$country <- toupper(GDP$country)
rename <- lm.cty_gaba%>%filter(Country %ni% GDP$country)
rename<-unique(rename$Country)
names(GDP)[names(GDP) == 'country'] <- "Country"
GDP[GDP$Country == "KOREA, REP.", "Country"] <- "SOUTH KOREA"
GDP[GDP$Country == "EGYPT, ARAB REP.", "Country"] <- "EGYPT"
GDP[GDP$Country == "RUSSIAN FEDERATION", "Country"] <- "RUSSIA"
GDP[GDP$Country == "SLOVAK REPUBLIC", "Country"] <- "SLOVAKIA"
GDP[GDP$Country == "VENEZUEL", "Country"] <- "VENEZUELA"
GDP[GDP$Country == "TURKEY", "Country"] <- "TÜRKİYE"
lm.cty_gaba$Year<-as.numeric(lm.cty_gaba$Year)
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Gabapentinoids")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
## [[1]]
## lower est.
## Year 0.1654085 0.1986732
## upper numDF denDF
## Year 0.2319379 1 198
## F-value p-value Model
## Year 138.7183 0 UMIC
## AAPC explower
## Year 21.97833 17.9875
## expupper
## Year 26.10414
##
## [[2]]
## lower est.
## Year 0.1149576 0.1296578
## upper numDF denDF
## Year 0.144358 1 376
## F-value p-value Model
## Year 300.7794 0 HIC
## AAPC explower
## Year 13.84387 12.18259
## expupper
## Year 15.52976
##
## [[3]]
## lower est.
## Year 0.1701379 0.2093103
## upper numDF denDF
## Year 0.2484826 1 59
## F-value
## Year 114.3178
## p-value
## Year 0.000000000000001998401
## Model AAPC explower
## Year LMIC 23.28275 18.54684
## expupper
## Year 28.20786
gabapentinoids.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
gabapentinoids.mlm_income$Drug<-"Gabapentinoids"
datatable(as.data.frame(gabapentinoids.mlm_income[,c(8:11,7)]),caption = "Average annual percentage change by income level", options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Gabapentin
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Gabapentin")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
gabapentin.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
gabapentin.mlm_income<-as.data.frame(gabapentin.mlm_income)
gabapentin.mlm_income$Drug<-"Gabapentin"
Gabapentin enacarbil
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Gabapentin Enacarbil")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
enacarbil.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
enacarbil.mlm_income<-as.data.frame(enacarbil.mlm_income)
enacarbil.mlm_income$Drug<-"Gabapentin Enacarbil"
Pregabalin
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Pregabalin")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
pregabalin.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
pregabalin.mlm_income$Drug<-"Pregabalin"
pregabalin.mlm_income<-as.data.frame(pregabalin.mlm_income)
Individual drugs income AAPC
drug_income_mlm<-rbind(gabapentinoids.mlm_income,gabapentin.mlm_income, enacarbil.mlm_income, pregabalin.mlm_income)
datatable(as.data.frame(drug_income_mlm[,c(12,8:11,7)]),caption = "Average annual percentage change by income level", options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
write.csv(drug_income_mlm,"D:/R/midas gaba/R1/Output_5_Subgroup_AAPC_income.csv")
Meta-analysis of DDD/TID by Year
When DDD=0 or extremely small, Country data wont be counted as number of studies in the meta analysis
options(width=800)
library(Rcpp)
library(meta)
library(data.table)
library(dplyr)
set_subzero<- left_join(analy,GDP,by = c("Country") )
set_subzero$DDD_dum=set_subzero$DDD
set_subzero$DDD_dum[set_subzero$DDD==0]<-0.00001
CIs<-pois.approx(set_subzero$DDD_dum, set_subzero$Population*365.25, conf.level = 0.95)
meta.gaba<-cbind(set_subzero,CIs)
meta.gaba$income <- factor(meta.gaba$income, levels =
c("HIC","UMIC","LMIC"))
meta <- function(rho, iseed){
meta.gaba_1<- subset(meta.gaba, Year==rho & Drug==iseed)
m1_var<-metagen(log(meta.gaba_1$rate),
lower = log(meta.gaba_1$lower),
upper = log(meta.gaba_1$upper),
studlab = meta.gaba_1$Country,
sm = "IRLN", method.tau = "DL",
comb.fixed = TRUE,
byvar = meta.gaba_1$income)
print(c(rho, iseed))
print(summary(m1_var), digits=4)
est.by.random<-c("Year", "DDD/TID", "DDD/TID - lower","DDD/TID - upper")
est.by.random$Year<-rho
est.by.random$Drug<-iseed
est.by.random$`DDD/TID`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$TE)))))
est.by.random$`DDD/TID - lower`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$lower)))))
est.by.random$`DDD/TID - upper`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$upper)))))
est.by.random$income<-(t(data.frame(as.list(((summary(m1_var))$byvar)))))
return(c(est.by.random))
}
datin <- expand.grid(rho = unique(meta.gaba$Year), iseed = unique(meta.gaba$Drug))
i <- 1:nrow(datin)
datout <- with(datin,
lapply(i, function(j){meta(rho[j], iseed[j])}))
## [1] 2008 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1047 [0.1045; 0.1049] 0.2 1.7 UMIC
## AUSTRALIA 0.7587 [0.7581; 0.7593] 0.7 1.7 HIC
## AUSTRIA 1.4440 [1.4427; 1.4454] 0.5 1.7 HIC
## BELARUS 0.0049 [0.0048; 0.0049] 0.0 1.7 UMIC
## BELGIUM 0.3490 [0.3484; 0.3495] 0.2 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0522 [0.0522; 0.0523] 0.5 1.7 UMIC
## BULGARIA 0.0867 [0.0863; 0.0870] 0.0 1.7 UMIC
## CANADA 2.4878 [2.4870; 2.4887] 3.7 1.7 HIC
## CHILE 0.0425 [0.0423; 0.0426] 0.0 1.7 HIC
## CHINA 0.0009 [0.0009; 0.0009] 0.1 1.7 UMIC
## COLOMBIA 0.0340 [0.0339; 0.0341] 0.1 1.7 UMIC
## CROATIA 0.2926 [0.2918; 0.2935] 0.1 1.7 HIC
## CZECH REPUBLIC 0.7153 [0.7145; 0.7162] 0.3 1.7 HIC
## ECUADOR 0.0727 [0.0725; 0.0730] 0.0 1.7 UMIC
## EGYPT 0.0759 [0.0758; 0.0760] 0.3 1.7 LMIC
## ESTONIA 0.0754 [0.0746; 0.0761] 0.0 1.7 HIC
## FINLAND 0.9593 [0.9579; 0.9607] 0.2 1.7 HIC
## FRANCE 1.3793 [1.3788; 1.3798] 3.9 1.7 HIC
## GERMANY 1.2294 [1.2290; 1.2298] 4.5 1.7 HIC
## GREECE 0.8335 [0.8326; 0.8344] 0.4 1.7 HIC
## HUNGARY 0.2259 [0.2254; 0.2264] 0.1 1.7 HIC
## INDIA 0.0320 [0.0319; 0.0320] 1.7 1.7 LMIC
## IRELAND 0.9193 [0.9178; 0.9207] 0.2 1.7 HIC
## ITALY 0.5709 [0.5706; 0.5712] 1.5 1.7 HIC
## JAPAN 0.1016 [0.1015; 0.1017] 0.6 1.7 HIC
## JORDAN 0.0755 [0.0751; 0.0758] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0298 [0.0294; 0.0301] 0.0 1.7 HIC
## LATVIA 0.3321 [0.3308; 0.3334] 0.0 1.7 HIC
## LEBANON 0.2416 [0.2409; 0.2424] 0.1 1.7 UMIC
## LITHUANIA 0.2187 [0.2179; 0.2195] 0.0 1.7 HIC
## LUXEMBOURG 0.8551 [0.8508; 0.8595] 0.0 1.7 HIC
## MEXICO 0.0713 [0.0712; 0.0713] 0.4 1.7 UMIC
## MOROCCO 0.0074 [0.0073; 0.0074] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.9749 [0.9734; 0.9765] 0.2 1.7 HIC
## NORWAY 1.1055 [1.1040; 1.1071] 0.2 1.7 HIC
## PAKISTAN 0.0542 [0.0542; 0.0543] 0.4 1.7 LMIC
## PERU 0.0242 [0.0241; 0.0243] 0.0 1.7 UMIC
## PHILIPPINES 0.0260 [0.0260; 0.0261] 0.1 1.7 LMIC
## POLAND 0.1470 [0.1468; 0.1472] 0.3 1.7 HIC
## PORTUGAL 1.3098 [1.3087; 1.3109] 0.6 1.7 HIC
## PUERTO RICO 3.7459 [3.7426; 3.7492] 0.6 1.7 HIC
## ROMANIA 0.0597 [0.0595; 0.0599] 0.1 1.7 UMIC
## RUSSIA 0.0152 [0.0152; 0.0152] 0.1 1.7 UMIC
## SAUDI ARABIA 0.1059 [0.1057; 0.1061] 0.1 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.5982 [0.5971; 0.5993] 0.1 1.7 HIC
## SLOVENIA 0.4548 [0.4533; 0.4564] 0.0 1.7 HIC
## SOUTH AFRICA 0.0515 [0.0514; 0.0516] 0.1 1.7 UMIC
## SOUTH KOREA 0.6427 [0.6424; 0.6431] 1.4 1.7 HIC
## SPAIN 1.7859 [1.7853; 1.7866] 3.7 1.7 HIC
## SWEDEN 1.2074 [1.2063; 1.2086] 0.5 1.7 HIC
## SWITZERLAND 0.6300 [0.6291; 0.6309] 0.2 1.7 HIC
## TAIWAN 0.1750 [0.1747; 0.1753] 0.2 1.7 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0646 [0.0643; 0.0648] 0.0 1.7 LMIC
## TÜRKIYE 1.1817 [1.1813; 1.1821] 3.8 1.7 UMIC
## UNITED ARAB EMIRATES 0.1001 [0.0997; 0.1005] 0.0 1.7 HIC
## UNITED KINGDOM 1.6870 [1.6864; 1.6875] 4.7 1.7 HIC
## UNITED STATES 4.4760 [4.4756; 4.4764] 61.3 1.7 HIC
## URUGUAY 0.1957 [0.1949; 0.1964] 0.0 1.7 HIC
## VENEZUELA 0.4496 [0.4492; 0.4500] 0.6 1.7 UMIC
##
## Number of studies combined: k = 59
##
## rate 95%-CI z p-value
## Common effect model 2.3447 [2.3446; 2.3449] 24236.81 0
## Random effects model 0.2084 [0.1451; 0.2996] -8.48 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.0195 [1.5801; 5.1102]; tau = 1.4211 [1.2570; 2.2606]
## I^2 = 100.0%; H = 4154.05
##
## Test of heterogeneity:
## Q d.f. p-value
## 1000855860.37 58 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 2.9227 [2.9225; 2.9229] 383220712.12 100.0%
## income = UMIC 16 0.4642 [0.4641; 0.4644] 100212177.30 100.0%
## income = LMIC 6 0.0380 [0.0379; 0.0380] 2327882.65 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 515095088.30 2 0
## Within groups 485760772.07 56 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.5040 [0.3676; 0.6909] 0.9588 0.9792
## income = UMIC 16 0.0532 [0.0208; 0.1360] 3.6641 1.9142
## income = LMIC 6 0.0343 [0.0236; 0.0500] 0.2205 0.4696
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 120.51 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1194 [0.1193; 0.1196] 0.2 1.7 UMIC
## AUSTRALIA 0.7770 [0.7764; 0.7776] 0.7 1.7 HIC
## AUSTRIA 1.5762 [1.5748; 1.5776] 0.5 1.7 HIC
## BELARUS 0.0093 [0.0092; 0.0094] 0.0 1.7 UMIC
## BELGIUM 0.3669 [0.3663; 0.3675] 0.2 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0590 [0.0590; 0.0591] 0.5 1.7 UMIC
## BULGARIA 0.1204 [0.1200; 0.1208] 0.0 1.7 UMIC
## CANADA 2.6364 [2.6355; 2.6373] 3.5 1.7 HIC
## CHILE 0.0335 [0.0333; 0.0336] 0.0 1.7 HIC
## CHINA 0.0020 [0.0020; 0.0020] 0.1 1.7 UMIC
## COLOMBIA 0.0287 [0.0286; 0.0288] 0.1 1.7 UMIC
## CROATIA 0.2979 [0.2971; 0.2988] 0.1 1.7 HIC
## CZECH REPUBLIC 0.8909 [0.8900; 0.8918] 0.4 1.7 HIC
## ECUADOR 0.0696 [0.0693; 0.0698] 0.0 1.7 UMIC
## EGYPT 0.0833 [0.0832; 0.0834] 0.3 1.7 LMIC
## ESTONIA 0.1091 [0.1081; 0.1100] 0.0 1.7 HIC
## FINLAND 0.9637 [0.9623; 0.9650] 0.2 1.7 HIC
## FRANCE 1.2998 [1.2993; 1.3003] 3.2 1.7 HIC
## GERMANY 1.3422 [1.3418; 1.3426] 4.3 1.7 HIC
## GREECE 0.7983 [0.7974; 0.7991] 0.3 1.7 HIC
## HUNGARY 0.2731 [0.2725; 0.2736] 0.1 1.7 HIC
## INDIA 0.0343 [0.0343; 0.0343] 1.7 1.7 LMIC
## IRELAND 0.9042 [0.9028; 0.9057] 0.2 1.7 HIC
## ITALY 0.5370 [0.5367; 0.5373] 1.3 1.7 HIC
## JAPAN 0.1478 [0.1477; 0.1479] 0.8 1.7 HIC
## JORDAN 0.0886 [0.0882; 0.0889] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0266 [0.0263; 0.0269] 0.0 1.7 HIC
## LATVIA 0.3719 [0.3705; 0.3732] 0.0 1.7 HIC
## LEBANON 0.3655 [0.3646; 0.3664] 0.1 1.7 UMIC
## LITHUANIA 0.2225 [0.2216; 0.2234] 0.0 1.7 HIC
## LUXEMBOURG 0.7788 [0.7747; 0.7829] 0.0 1.7 HIC
## MEXICO 0.0705 [0.0704; 0.0706] 0.3 1.7 UMIC
## MOROCCO 0.0051 [0.0050; 0.0051] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.1650 [1.1633; 1.1667] 0.2 1.7 HIC
## NORWAY 1.4251 [1.4233; 1.4268] 0.3 1.7 HIC
## PAKISTAN 0.0466 [0.0465; 0.0466] 0.3 1.7 LMIC
## PERU 0.0173 [0.0173; 0.0174] 0.0 1.7 UMIC
## PHILIPPINES 0.0266 [0.0265; 0.0266] 0.1 1.7 LMIC
## POLAND 0.1674 [0.1672; 0.1676] 0.3 1.7 HIC
## PORTUGAL 1.1282 [1.1271; 1.1292] 0.5 1.7 HIC
## PUERTO RICO 4.7038 [4.7000; 4.7075] 0.7 1.7 HIC
## ROMANIA 0.1543 [0.1540; 0.1546] 0.1 1.7 UMIC
## RUSSIA 0.0166 [0.0166; 0.0166] 0.1 1.7 UMIC
## SAUDI ARABIA 0.1008 [0.1006; 0.1010] 0.1 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.7215 [0.7203; 0.7227] 0.2 1.7 HIC
## SLOVENIA 0.4132 [0.4117; 0.4146] 0.0 1.7 HIC
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.7 UMIC
## SOUTH KOREA 0.6597 [0.6593; 0.6601] 1.3 1.7 HIC
## SPAIN 1.8188 [1.8181; 1.8194] 3.4 1.7 HIC
## SWEDEN 1.2153 [1.2141; 1.2165] 0.4 1.7 HIC
## SWITZERLAND 0.5953 [0.5944; 0.5962] 0.2 1.7 HIC
## TAIWAN 0.1779 [0.1776; 0.1781] 0.2 1.7 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0831 [0.0828; 0.0834] 0.0 1.7 LMIC
## TÜRKIYE 1.4377 [1.4372; 1.4381] 4.1 1.7 UMIC
## UNITED ARAB EMIRATES 0.1115 [0.1111; 0.1119] 0.0 1.7 HIC
## UNITED KINGDOM 2.0046 [2.0040; 2.0052] 5.0 1.7 HIC
## UNITED STATES 5.1806 [5.1802; 5.1811] 62.9 1.7 HIC
## URUGUAY 0.2686 [0.2677; 0.2695] 0.0 1.7 HIC
## VENEZUELA 0.4848 [0.4844; 0.4852] 0.5 1.7 UMIC
##
## Number of studies combined: k = 59
##
## rate 95%-CI z p-value
## Common effect model 2.7150 [2.7149; 2.7152] 30310.89 0
## Random effects model 0.2289 [0.1574; 0.3328] -7.72 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.1545 [1.6226; 5.2873]; tau = 1.4678 [1.2738; 2.2994]
## I^2 = 100.0%; H = 4506.10
##
## Test of heterogeneity:
## Q d.f. p-value
## 1177686897.84 58 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 3.3765 [3.3762; 3.3767] 468190078.46 100.0%
## income = UMIC 16 0.5611 [0.5610; 0.5613] 130673937.40 100.0%
## income = LMIC 6 0.0394 [0.0394; 0.0394] 2325753.04 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 576497128.94 2 0
## Within groups 601189768.89 56 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.5381 [0.3850; 0.7520] 1.0788 1.0386
## income = UMIC 16 0.0649 [0.0241; 0.1751] 4.0969 2.0241
## income = LMIC 6 0.0338 [0.0233; 0.0491] 0.2179 0.4668
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 119.84 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1292 [0.1290; 0.1294] 0.2 1.7 UMIC
## AUSTRALIA 0.8366 [0.8360; 0.8373] 0.6 1.7 HIC
## AUSTRIA 1.7240 [1.7225; 1.7255] 0.5 1.7 HIC
## BELARUS 0.0122 [0.0121; 0.0124] 0.0 1.7 UMIC
## BELGIUM 0.4468 [0.4461; 0.4475] 0.2 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0614 [0.0614; 0.0615] 0.4 1.7 UMIC
## BULGARIA 0.1616 [0.1611; 0.1621] 0.0 1.7 UMIC
## CANADA 2.7823 [2.7814; 2.7832] 3.2 1.7 HIC
## CHILE 0.0269 [0.0268; 0.0271] 0.0 1.7 HIC
## CHINA 0.0031 [0.0031; 0.0031] 0.1 1.7 UMIC
## COLOMBIA 0.0235 [0.0234; 0.0236] 0.0 1.7 UMIC
## CROATIA 0.2668 [0.2660; 0.2676] 0.0 1.7 HIC
## CZECH REPUBLIC 1.0463 [1.0452; 1.0473] 0.4 1.7 HIC
## ECUADOR 0.0720 [0.0718; 0.0723] 0.0 1.7 UMIC
## EGYPT 0.1069 [0.1067; 0.1070] 0.3 1.7 LMIC
## ESTONIA 0.2425 [0.2411; 0.2439] 0.0 1.7 HIC
## FINLAND 0.9824 [0.9810; 0.9837] 0.2 1.7 HIC
## FRANCE 1.2534 [1.2529; 1.2538] 2.6 1.7 HIC
## GERMANY 1.4106 [1.4101; 1.4110] 3.8 1.7 HIC
## GREECE 0.8032 [0.8023; 0.8041] 0.3 1.7 HIC
## HUNGARY 0.3559 [0.3553; 0.3565] 0.1 1.7 HIC
## INDIA 0.0373 [0.0373; 0.0373] 1.5 1.7 LMIC
## IRELAND 0.8461 [0.8447; 0.8475] 0.1 1.7 HIC
## ITALY 0.5452 [0.5449; 0.5455] 1.1 1.7 HIC
## JAPAN 0.1810 [0.1809; 0.1811] 0.8 1.7 HIC
## JORDAN 0.1086 [0.1082; 0.1090] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0240 [0.0237; 0.0243] 0.0 1.7 HIC
## LATVIA 0.4715 [0.4699; 0.4730] 0.0 1.7 HIC
## LEBANON 0.3637 [0.3629; 0.3646] 0.1 1.7 UMIC
## LITHUANIA 0.2311 [0.2302; 0.2320] 0.0 1.7 HIC
## LUXEMBOURG 0.7427 [0.7388; 0.7466] 0.0 1.7 HIC
## MEXICO 0.0781 [0.0780; 0.0782] 0.3 1.7 UMIC
## MOROCCO 0.0060 [0.0060; 0.0061] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.3931 [1.3913; 1.3950] 0.2 1.7 HIC
## NORWAY 1.8065 [1.8046; 1.8085] 0.3 1.7 HIC
## PAKISTAN 0.0446 [0.0445; 0.0446] 0.3 1.7 LMIC
## PERU 0.0172 [0.0171; 0.0173] 0.0 1.7 UMIC
## PHILIPPINES 0.0290 [0.0289; 0.0291] 0.1 1.7 LMIC
## POLAND 0.1920 [0.1917; 0.1922] 0.2 1.7 HIC
## PORTUGAL 1.1608 [1.1597; 1.1618] 0.4 1.7 HIC
## PUERTO RICO 5.5328 [5.5288; 5.5368] 0.7 1.7 HIC
## ROMANIA 0.2463 [0.2459; 0.2466] 0.2 1.7 UMIC
## RUSSIA 0.0159 [0.0159; 0.0160] 0.1 1.7 UMIC
## SAUDI ARABIA 0.1232 [0.1230; 0.1234] 0.1 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.8791 [0.8778; 0.8805] 0.2 1.7 HIC
## SLOVENIA 0.4079 [0.4064; 0.4093] 0.0 1.7 HIC
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.7 UMIC
## SOUTH KOREA 0.6761 [0.6757; 0.6765] 1.1 1.7 HIC
## SPAIN 1.8213 [1.8206; 1.8219] 2.9 1.7 HIC
## SWEDEN 1.2623 [1.2611; 1.2635] 0.4 1.7 HIC
## SWITZERLAND 0.5748 [0.5739; 0.5757] 0.1 1.7 HIC
## TAIWAN 0.1970 [0.1967; 0.1973] 0.2 1.7 HIC
## THAILAND 0.1721 [0.1720; 0.1723] 0.4 1.7 UMIC
## TUNISIA 0.0815 [0.0812; 0.0818] 0.0 1.7 LMIC
## TÜRKIYE 1.7746 [1.7740; 1.7751] 4.3 1.7 UMIC
## UNITED ARAB EMIRATES 0.1305 [0.1301; 0.1309] 0.0 1.7 HIC
## UNITED KINGDOM 2.3586 [2.3580; 2.3592] 5.0 1.7 HIC
## UNITED STATES 6.3255 [6.3250; 6.3260] 65.3 1.7 HIC
## URUGUAY 0.2401 [0.2392; 0.2409] 0.0 1.7 HIC
## VENEZUELA 0.4784 [0.4780; 0.4788] 0.5 1.7 UMIC
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.2965 [3.2963; 3.2967] 39447.96 0
## Random effects model 0.2505 [0.1693; 0.3707] -6.92 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.3975 [1.6822; 5.4854]; tau = 1.5484 [1.2970; 2.3421]
## I^2 = 100.0%; H = 5009.64
##
## Test of heterogeneity:
## Q d.f. p-value
## 1480692150.09 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 4.1294 [4.1291; 4.1296] 602945599.95 100.0%
## income = UMIC 17 0.6470 [0.6469; 0.6472] 173981199.85 100.0%
## income = LMIC 6 0.0436 [0.0436; 0.0436] 3571962.37 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 700193387.92 2 0
## Within groups 780498762.17 57 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.5882 [0.4093; 0.8453] 1.2663 1.1253
## income = UMIC 17 0.0769 [0.0292; 0.2024] 4.1520 2.0377
## income = LMIC 6 0.0369 [0.0238; 0.0572] 0.2998 0.5476
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 94.14 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1364 [0.1363; 0.1366] 0.2 1.6 UMIC
## AUSTRALIA 0.8668 [0.8662; 0.8675] 0.6 1.6 HIC
## AUSTRIA 1.8391 [1.8376; 1.8406] 0.5 1.6 HIC
## BELARUS 0.0139 [0.0138; 0.0140] 0.0 1.6 UMIC
## BELGIUM 0.4884 [0.4878; 0.4891] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0443 [0.0439; 0.0447] 0.0 1.6 UMIC
## BRAZIL 0.0770 [0.0769; 0.0770] 0.5 1.6 UMIC
## BULGARIA 0.2211 [0.2206; 0.2217] 0.1 1.6 UMIC
## CANADA 2.9905 [2.9896; 2.9915] 3.2 1.6 HIC
## CHILE 0.0234 [0.0233; 0.0235] 0.0 1.6 HIC
## CHINA 0.0047 [0.0047; 0.0047] 0.2 1.6 UMIC
## COLOMBIA 0.0219 [0.0218; 0.0220] 0.0 1.6 UMIC
## CROATIA 0.2282 [0.2275; 0.2290] 0.0 1.6 HIC
## CZECH REPUBLIC 1.2108 [1.2097; 1.2119] 0.4 1.6 HIC
## ECUADOR 0.0672 [0.0670; 0.0675] 0.0 1.6 UMIC
## EGYPT 0.1148 [0.1147; 0.1149] 0.3 1.6 LMIC
## ESTONIA 0.2902 [0.2887; 0.2917] 0.0 1.6 HIC
## FINLAND 1.0435 [1.0420; 1.0449] 0.2 1.6 HIC
## FRANCE 1.2042 [1.2038; 1.2047] 2.4 1.6 HIC
## GERMANY 1.4962 [1.4957; 1.4966] 3.8 1.6 HIC
## GREECE 0.8916 [0.8906; 0.8925] 0.3 1.6 HIC
## HUNGARY 0.4230 [0.4223; 0.4236] 0.1 1.6 HIC
## INDIA 0.0430 [0.0430; 0.0430] 1.7 1.6 LMIC
## IRELAND 0.8952 [0.8938; 0.8967] 0.1 1.6 HIC
## ITALY 0.5448 [0.5445; 0.5451] 1.0 1.6 HIC
## JAPAN 0.1505 [0.1504; 0.1506] 0.6 1.6 HIC
## JORDAN 0.1083 [0.1079; 0.1087] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0189 [0.0187; 0.0190] 0.0 1.6 UMIC
## KUWAIT 0.0285 [0.0282; 0.0288] 0.0 1.6 HIC
## LATVIA 0.6091 [0.6073; 0.6108] 0.0 1.6 HIC
## LEBANON 0.3338 [0.3330; 0.3346] 0.1 1.6 UMIC
## LITHUANIA 0.2378 [0.2369; 0.2387] 0.0 1.6 HIC
## LUXEMBOURG 0.7418 [0.7379; 0.7456] 0.0 1.6 HIC
## MEXICO 0.0770 [0.0769; 0.0771] 0.3 1.6 UMIC
## MOROCCO 0.0104 [0.0103; 0.0104] 0.0 1.6 LMIC
## NETHERLANDS 0.6528 [0.6522; 0.6534] 0.3 1.6 HIC
## NEW ZEALAND 1.6064 [1.6045; 1.6084] 0.2 1.6 HIC
## NORWAY 2.0397 [2.0376; 2.0418] 0.3 1.6 HIC
## PAKISTAN 0.0465 [0.0465; 0.0466] 0.3 1.6 LMIC
## PERU 0.0192 [0.0192; 0.0193] 0.0 1.6 UMIC
## PHILIPPINES 0.0279 [0.0279; 0.0280] 0.1 1.6 LMIC
## POLAND 0.2211 [0.2208; 0.2213] 0.3 1.6 HIC
## PORTUGAL 1.1147 [1.1136; 1.1157] 0.4 1.6 HIC
## PUERTO RICO 7.1769 [7.1723; 7.1815] 0.8 1.6 HIC
## ROMANIA 0.3992 [0.3987; 0.3996] 0.3 1.6 UMIC
## RUSSIA 0.0184 [0.0183; 0.0184] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1683 [0.1681; 0.1686] 0.1 1.6 HIC
## SERBIA 0.0660 [0.0657; 0.0663] 0.0 1.6 UMIC
## SLOVAKIA 0.9711 [0.9697; 0.9724] 0.2 1.6 HIC
## SLOVENIA 0.3702 [0.3688; 0.3715] 0.0 1.6 HIC
## SOUTH AFRICA 0.0503 [0.0502; 0.0504] 0.1 1.6 UMIC
## SOUTH KOREA 0.6730 [0.6727; 0.6734] 1.0 1.6 HIC
## SPAIN 1.8636 [1.8630; 1.8643] 2.7 1.6 HIC
## SWEDEN 1.4097 [1.4084; 1.4109] 0.4 1.6 HIC
## SWITZERLAND 0.5369 [0.5360; 0.5377] 0.1 1.6 HIC
## TAIWAN 0.2174 [0.2171; 0.2177] 0.2 1.6 HIC
## THAILAND 0.2549 [0.2547; 0.2551] 0.5 1.6 UMIC
## TUNISIA 0.0891 [0.0888; 0.0894] 0.0 1.6 LMIC
## TÜRKIYE 2.1980 [2.1975; 2.1986] 5.0 1.6 UMIC
## UNITED ARAB EMIRATES 0.1306 [0.1302; 0.1310] 0.0 1.6 HIC
## UNITED KINGDOM 2.7146 [2.7139; 2.7152] 5.4 1.6 HIC
## UNITED STATES 6.5599 [6.5594; 6.5604] 63.7 1.6 HIC
## URUGUAY 0.3039 [0.3029; 0.3049] 0.0 1.6 HIC
## VENEZUELA 0.5095 [0.5091; 0.5100] 0.5 1.6 UMIC
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.3835 [3.3833; 3.3837] 41728.55 0
## Random effects model 0.2547 [0.1748; 0.3712] -7.12 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.3641 [1.7027; 5.3863]; tau = 1.5376 [1.3049; 2.3208]
## I^2 = 100.0%; H = 5068.59
##
## Test of heterogeneity:
## Q d.f. p-value
## 1618509242.42 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 4.2949 [4.2946; 4.2951] 635255138.42 100.0%
## income = UMIC 20 0.7904 [0.7902; 0.7905] 226182602.13 100.0%
## income = LMIC 6 0.0486 [0.0486; 0.0486] 3641921.59 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 753429580.28 2 0
## Within groups 865079662.14 61 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.6297 [0.4422; 0.8968] 1.2366 1.1120
## income = UMIC 20 0.0780 [0.0314; 0.1941] 4.3236 2.0793
## income = LMIC 6 0.0426 [0.0279; 0.0650] 0.2800 0.5291
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 95.75 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1383 [0.1382; 0.1385] 0.2 1.6 UMIC
## AUSTRALIA 0.9460 [0.9453; 0.9466] 0.7 1.6 HIC
## AUSTRIA 1.9626 [1.9611; 1.9642] 0.5 1.6 HIC
## BELARUS 0.0148 [0.0147; 0.0150] 0.0 1.6 UMIC
## BELGIUM 0.5723 [0.5715; 0.5730] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0958 [0.0953; 0.0964] 0.0 1.6 UMIC
## BRAZIL 0.0876 [0.0875; 0.0876] 0.5 1.6 UMIC
## BULGARIA 0.2866 [0.2859; 0.2872] 0.1 1.6 UMIC
## CANADA 3.3044 [3.3034; 3.3054] 3.5 1.6 HIC
## CHILE 0.0220 [0.0219; 0.0221] 0.0 1.6 HIC
## CHINA 0.0083 [0.0083; 0.0083] 0.4 1.6 UMIC
## COLOMBIA 0.0193 [0.0192; 0.0193] 0.0 1.6 UMIC
## CROATIA 0.2042 [0.2035; 0.2049] 0.0 1.6 HIC
## CZECH REPUBLIC 1.3418 [1.3407; 1.3430] 0.4 1.6 HIC
## ECUADOR 0.0590 [0.0588; 0.0592] 0.0 1.6 UMIC
## EGYPT 0.1044 [0.1043; 0.1045] 0.3 1.6 LMIC
## ESTONIA 0.3901 [0.3883; 0.3919] 0.0 1.6 HIC
## FINLAND 1.1761 [1.1746; 1.1776] 0.2 1.6 HIC
## FRANCE 1.2173 [1.2168; 1.2177] 2.4 1.6 HIC
## GERMANY 1.5603 [1.5599; 1.5608] 3.9 1.6 HIC
## GREECE 0.8239 [0.8230; 0.8248] 0.3 1.6 HIC
## HUNGARY 0.4641 [0.4634; 0.4648] 0.1 1.6 HIC
## INDIA 0.0472 [0.0472; 0.0473] 1.8 1.6 LMIC
## IRELAND 0.9214 [0.9199; 0.9228] 0.1 1.6 HIC
## ITALY 0.5310 [0.5307; 0.5313] 1.0 1.6 HIC
## JAPAN 0.1244 [0.1243; 0.1245] 0.5 1.6 HIC
## JORDAN 0.0998 [0.0994; 0.1002] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0218 [0.0217; 0.0220] 0.0 1.6 UMIC
## KUWAIT 0.0292 [0.0289; 0.0295] 0.0 1.6 HIC
## LATVIA 0.8136 [0.8115; 0.8156] 0.1 1.6 HIC
## LEBANON 0.2992 [0.2985; 0.3000] 0.1 1.6 UMIC
## LITHUANIA 0.2617 [0.2607; 0.2626] 0.0 1.6 HIC
## LUXEMBOURG 0.7475 [0.7437; 0.7514] 0.0 1.6 HIC
## MEXICO 0.0837 [0.0836; 0.0837] 0.3 1.6 UMIC
## MOROCCO 0.0172 [0.0171; 0.0173] 0.0 1.6 LMIC
## NETHERLANDS 0.6605 [0.6599; 0.6612] 0.3 1.6 HIC
## NEW ZEALAND 1.8861 [1.8840; 1.8882] 0.3 1.6 HIC
## NORWAY 2.1749 [2.1727; 2.1770] 0.3 1.6 HIC
## PAKISTAN 0.0469 [0.0469; 0.0470] 0.3 1.6 LMIC
## PERU 0.0190 [0.0189; 0.0191] 0.0 1.6 UMIC
## PHILIPPINES 0.0268 [0.0267; 0.0268] 0.1 1.6 LMIC
## POLAND 0.2055 [0.2053; 0.2058] 0.2 1.6 HIC
## PORTUGAL 1.1942 [1.1931; 1.1953] 0.4 1.6 HIC
## PUERTO RICO 8.2103 [8.2053; 8.2152] 0.9 1.6 HIC
## ROMANIA 0.5151 [0.5146; 0.5157] 0.3 1.6 UMIC
## RUSSIA 0.0226 [0.0225; 0.0226] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1279 [0.1277; 0.1281] 0.1 1.6 HIC
## SERBIA 0.0827 [0.0823; 0.0830] 0.0 1.6 UMIC
## SLOVAKIA 1.0650 [1.0635; 1.0664] 0.2 1.6 HIC
## SLOVENIA 0.3544 [0.3531; 0.3558] 0.0 1.6 HIC
## SOUTH AFRICA 0.0532 [0.0531; 0.0533] 0.1 1.6 UMIC
## SOUTH KOREA 0.7229 [0.7225; 0.7232] 1.1 1.6 HIC
## SPAIN 1.8152 [1.8146; 1.8159] 2.6 1.6 HIC
## SWEDEN 1.5277 [1.5265; 1.5290] 0.4 1.6 HIC
## SWITZERLAND 0.5176 [0.5168; 0.5184] 0.1 1.6 HIC
## TAIWAN 0.2142 [0.2139; 0.2145] 0.2 1.6 HIC
## THAILAND 0.2950 [0.2948; 0.2952] 0.6 1.6 UMIC
## TUNISIA 0.1064 [0.1061; 0.1067] 0.0 1.6 LMIC
## TÜRKIYE 2.3781 [2.3775; 2.3787] 5.4 1.6 UMIC
## UNITED ARAB EMIRATES 0.0810 [0.0807; 0.0813] 0.0 1.6 HIC
## UNITED KINGDOM 3.2081 [3.2074; 3.2089] 6.3 1.6 HIC
## UNITED STATES 6.3591 [6.3586; 6.3595] 61.2 1.6 HIC
## URUGUAY 0.4181 [0.4169; 0.4192] 0.0 1.6 HIC
## VENEZUELA 0.5864 [0.5860; 0.5869] 0.5 1.6 UMIC
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.2768 [3.2767; 3.2770] 40970.80 0
## Random effects model 0.2735 [0.1891; 0.3955] -6.89 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.2668 [1.7021; 5.3176]; tau = 1.5056 [1.3047; 2.3060]
## I^2 = 100.0%; H = 5128.07
##
## Test of heterogeneity:
## Q d.f. p-value
## 1656717916.72 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 4.2291 [4.2288; 4.2293] 594103079.71 100.0%
## income = UMIC 20 0.8095 [0.8093; 0.8096] 268589059.88 100.0%
## income = LMIC 6 0.0508 [0.0508; 0.0508] 2709682.56 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 791316094.57 2 0
## Within groups 865401822.15 61 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.6506 [0.4666; 0.9072] 1.0932 1.0456
## income = UMIC 20 0.0892 [0.0357; 0.2228] 4.3641 2.0890
## income = LMIC 6 0.0474 [0.0330; 0.0681] 0.2046 0.4523
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 111.79 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1348 [0.1346; 0.1350] 0.2 1.6 UMIC
## AUSTRALIA 0.7847 [0.7841; 0.7853] 0.5 1.6 HIC
## AUSTRIA 2.0660 [2.0644; 2.0676] 0.5 1.6 HIC
## BELARUS 0.0221 [0.0219; 0.0222] 0.0 1.6 UMIC
## BELGIUM 0.8101 [0.8093; 0.8110] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.1360 [0.1353; 0.1366] 0.0 1.6 UMIC
## BRAZIL 0.0978 [0.0977; 0.0978] 0.5 1.6 UMIC
## BULGARIA 0.4003 [0.3995; 0.4010] 0.1 1.6 UMIC
## CANADA 3.6682 [3.6672; 3.6693] 3.5 1.6 HIC
## CHILE 0.0206 [0.0205; 0.0207] 0.0 1.6 HIC
## CHINA 0.0119 [0.0119; 0.0119] 0.4 1.6 UMIC
## COLOMBIA 0.0168 [0.0167; 0.0169] 0.0 1.6 UMIC
## CROATIA 0.1891 [0.1884; 0.1897] 0.0 1.6 HIC
## CZECH REPUBLIC 1.5003 [1.4991; 1.5015] 0.4 1.6 HIC
## ECUADOR 0.0490 [0.0488; 0.0492] 0.0 1.6 UMIC
## EGYPT 0.1103 [0.1102; 0.1104] 0.3 1.6 LMIC
## ESTONIA 0.5081 [0.5061; 0.5101] 0.0 1.6 HIC
## FINLAND 1.4016 [1.4000; 1.4032] 0.2 1.6 HIC
## FRANCE 1.2245 [1.2240; 1.2249] 2.1 1.6 HIC
## GERMANY 1.6432 [1.6427; 1.6436] 3.6 1.6 HIC
## GREECE 0.9298 [0.9289; 0.9308] 0.3 1.6 HIC
## HUNGARY 0.5382 [0.5374; 0.5389] 0.1 1.6 HIC
## INDIA 0.0518 [0.0517; 0.0518] 1.8 1.6 LMIC
## IRELAND 0.9678 [0.9663; 0.9693] 0.1 1.6 HIC
## ITALY 0.5287 [0.5284; 0.5290] 0.9 1.6 HIC
## JAPAN 0.1114 [0.1113; 0.1115] 0.4 1.6 HIC
## JORDAN 0.1073 [0.1069; 0.1076] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0203 [0.0202; 0.0204] 0.0 1.6 UMIC
## KUWAIT 0.0408 [0.0404; 0.0411] 0.0 1.6 HIC
## LATVIA 1.0487 [1.0464; 1.0510] 0.1 1.6 HIC
## LEBANON 0.3187 [0.3180; 0.3195] 0.1 1.6 UMIC
## LITHUANIA 0.3129 [0.3118; 0.3139] 0.0 1.6 HIC
## LUXEMBOURG 0.7067 [0.7030; 0.7104] 0.0 1.6 HIC
## MEXICO 0.0904 [0.0903; 0.0905] 0.3 1.6 UMIC
## MOROCCO 0.0270 [0.0269; 0.0271] 0.0 1.6 LMIC
## NETHERLANDS 0.6591 [0.6585; 0.6598] 0.3 1.6 HIC
## NEW ZEALAND 2.1377 [2.1355; 2.1399] 0.3 1.6 HIC
## NORWAY 2.2712 [2.2690; 2.2734] 0.3 1.6 HIC
## PAKISTAN 0.0450 [0.0449; 0.0450] 0.2 1.6 LMIC
## PERU 0.0181 [0.0180; 0.0182] 0.0 1.6 UMIC
## PHILIPPINES 0.0330 [0.0330; 0.0331] 0.1 1.6 LMIC
## POLAND 0.2660 [0.2658; 0.2663] 0.3 1.6 HIC
## PORTUGAL 1.2241 [1.2230; 1.2252] 0.3 1.6 HIC
## PUERTO RICO 8.3869 [8.3819; 8.3919] 0.8 1.6 HIC
## ROMANIA 0.6903 [0.6897; 0.6909] 0.4 1.6 UMIC
## RUSSIA 0.0296 [0.0295; 0.0296] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1192 [0.1190; 0.1194] 0.1 1.6 HIC
## SERBIA 0.0894 [0.0891; 0.0897] 0.0 1.6 UMIC
## SLOVAKIA 1.1862 [1.1847; 1.1877] 0.2 1.6 HIC
## SLOVENIA 0.3501 [0.3488; 0.3515] 0.0 1.6 HIC
## SOUTH AFRICA 0.0572 [0.0571; 0.0573] 0.1 1.6 UMIC
## SOUTH KOREA 0.7358 [0.7354; 0.7362] 1.0 1.6 HIC
## SPAIN 1.8345 [1.8339; 1.8352] 2.3 1.6 HIC
## SWEDEN 1.8971 [1.8957; 1.8986] 0.5 1.6 HIC
## SWITZERLAND 0.5102 [0.5094; 0.5110] 0.1 1.6 HIC
## TAIWAN 0.2000 [0.1997; 0.2003] 0.1 1.6 HIC
## THAILAND 0.4366 [0.4363; 0.4368] 0.8 1.6 UMIC
## TUNISIA 0.1107 [0.1104; 0.1111] 0.0 1.6 LMIC
## TÜRKIYE 2.5445 [2.5439; 2.5451] 5.2 1.6 UMIC
## UNITED ARAB EMIRATES 0.0828 [0.0825; 0.0831] 0.0 1.6 HIC
## UNITED KINGDOM 3.8409 [3.8401; 3.8417] 6.7 1.6 HIC
## UNITED STATES 7.3525 [7.3520; 7.3530] 62.5 1.6 HIC
## URUGUAY 0.4117 [0.4106; 0.4128] 0.0 1.6 HIC
## VENEZUELA 0.6841 [0.6836; 0.6846] 0.5 1.6 UMIC
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.7911 [3.7909; 3.7913] 49132.25 0
## Random effects model 0.3009 [0.2060; 0.4396] -6.21 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.3923 [1.7292; 5.4420]; tau = 1.5467 [1.3150; 2.3328]
## I^2 = 100.0%; H = 5554.13
##
## Test of heterogeneity:
## Q d.f. p-value
## 1943447617.03 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 4.9391 [4.9388; 4.9393] 690808095.36 100.0%
## income = UMIC 20 0.8401 [0.8400; 0.8403] 303810679.88 100.0%
## income = LMIC 6 0.0547 [0.0547; 0.0548] 2636487.56 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 946192354.23 2 0
## Within groups 997255262.80 61 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.6983 [0.4959; 0.9833] 1.1591 1.0766
## income = UMIC 20 0.1017 [0.0421; 0.2456] 4.0472 2.0118
## income = LMIC 6 0.0542 [0.0384; 0.0764] 0.1842 0.4291
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 108.72 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0031 [ 0.0031; 0.0032] 0.0 1.5 UMIC
## ARGENTINA 0.1286 [ 0.1284; 0.1288] 0.1 1.5 UMIC
## AUSTRALIA 0.6459 [ 0.6453; 0.6464] 0.4 1.5 HIC
## AUSTRIA 2.1770 [ 2.1754; 2.1786] 0.5 1.5 HIC
## BELARUS 0.0305 [ 0.0303; 0.0307] 0.0 1.5 UMIC
## BELGIUM 1.0163 [ 1.0154; 1.0173] 0.3 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1421 [ 0.1414; 0.1427] 0.0 1.5 UMIC
## BRAZIL 0.1063 [ 0.1062; 0.1064] 0.5 1.5 UMIC
## BULGARIA 0.4875 [ 0.4867; 0.4884] 0.1 1.5 UMIC
## CANADA 3.8718 [ 3.8707; 3.8729] 3.4 1.5 HIC
## CHILE 0.0202 [ 0.0201; 0.0203] 0.0 1.5 HIC
## CHINA 0.0158 [ 0.0158; 0.0158] 0.5 1.5 UMIC
## COLOMBIA 0.0150 [ 0.0150; 0.0151] 0.0 1.5 UMIC
## CROATIA 0.1837 [ 0.1830; 0.1844] 0.0 1.5 HIC
## CZECH REPUBLIC 1.7140 [ 1.7127; 1.7153] 0.4 1.5 HIC
## ECUADOR 0.0434 [ 0.0432; 0.0436] 0.0 1.5 UMIC
## EGYPT 0.1243 [ 0.1242; 0.1244] 0.3 1.5 LMIC
## ESTONIA 0.6371 [ 0.6349; 0.6394] 0.0 1.5 HIC
## FINLAND 1.6351 [ 1.6333; 1.6368] 0.2 1.5 HIC
## FRANCE 1.2639 [ 1.2634; 1.2644] 2.0 1.5 HIC
## GERMANY 1.6853 [ 1.6848; 1.6858] 3.4 1.5 HIC
## GREECE 0.9639 [ 0.9629; 0.9649] 0.3 1.5 HIC
## HUNGARY 0.6103 [ 0.6095; 0.6111] 0.1 1.5 HIC
## INDIA 0.0637 [ 0.0637; 0.0637] 2.0 1.5 LMIC
## IRELAND 1.0037 [ 1.0022; 1.0052] 0.1 1.5 HIC
## ITALY 0.5519 [ 0.5516; 0.5522] 0.8 1.5 HIC
## JAPAN 0.1038 [ 0.1037; 0.1039] 0.3 1.5 HIC
## JORDAN 0.0884 [ 0.0880; 0.0887] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0272 [ 0.0270; 0.0273] 0.0 1.5 UMIC
## KUWAIT 0.1028 [ 0.1022; 0.1033] 0.0 1.5 HIC
## LATVIA 1.2957 [ 1.2931; 1.2983] 0.1 1.5 HIC
## LEBANON 0.3372 [ 0.3365; 0.3380] 0.1 1.5 UMIC
## LITHUANIA 0.3355 [ 0.3344; 0.3366] 0.0 1.5 HIC
## LUXEMBOURG 0.6660 [ 0.6625; 0.6696] 0.0 1.5 HIC
## MEXICO 0.0891 [ 0.0890; 0.0892] 0.3 1.5 UMIC
## MOROCCO 0.0311 [ 0.0310; 0.0312] 0.0 1.5 LMIC
## NETHERLANDS 0.6984 [ 0.6977; 0.6991] 0.3 1.5 HIC
## NEW ZEALAND 2.4379 [ 2.4355; 2.4403] 0.3 1.5 HIC
## NORWAY 2.2095 [ 2.2074; 2.2116] 0.3 1.5 HIC
## PAKISTAN 0.0423 [ 0.0422; 0.0423] 0.2 1.5 LMIC
## PERU 0.0158 [ 0.0157; 0.0159] 0.0 1.5 UMIC
## PHILIPPINES 0.0272 [ 0.0271; 0.0272] 0.1 1.5 LMIC
## POLAND 0.3332 [ 0.3329; 0.3335] 0.3 1.5 HIC
## PORTUGAL 1.2655 [ 1.2643; 1.2666] 0.3 1.5 HIC
## PUERTO RICO 10.1429 [10.1373; 10.1484] 0.9 1.5 HIC
## ROMANIA 0.8419 [ 0.8412; 0.8426] 0.4 1.5 UMIC
## RUSSIA 0.0362 [ 0.0361; 0.0362] 0.1 1.5 UMIC
## SAUDI ARABIA 0.1250 [ 0.1248; 0.1252] 0.1 1.5 HIC
## SERBIA 0.0908 [ 0.0905; 0.0912] 0.0 1.5 UMIC
## SLOVAKIA 1.4205 [ 1.4189; 1.4222] 0.2 1.5 HIC
## SLOVENIA 0.3401 [ 0.3388; 0.3414] 0.0 1.5 HIC
## SOUTH AFRICA 0.0581 [ 0.0580; 0.0582] 0.1 1.5 UMIC
## SOUTH KOREA 0.8166 [ 0.8162; 0.8170] 1.0 1.5 HIC
## SPAIN 1.8863 [ 1.8856; 1.8869] 2.2 1.5 HIC
## SWEDEN 2.3524 [ 2.3508; 2.3540] 0.6 1.5 HIC
## SWITZERLAND 0.5117 [ 0.5109; 0.5125] 0.1 1.5 HIC
## TAIWAN 0.1917 [ 0.1914; 0.1920] 0.1 1.5 HIC
## THAILAND 0.4858 [ 0.4856; 0.4861] 0.8 1.5 UMIC
## TUNISIA 0.1231 [ 0.1227; 0.1234] 0.0 1.5 LMIC
## TÜRKIYE 2.5723 [ 2.5717; 2.5729] 4.9 1.5 UMIC
## UNITED ARAB EMIRATES 0.0897 [ 0.0893; 0.0900] 0.0 1.5 HIC
## UNITED KINGDOM 4.4910 [ 4.4902; 4.4919] 7.3 1.5 HIC
## UNITED STATES 7.9211 [ 7.9206; 7.9216] 62.3 1.5 HIC
## URUGUAY 0.4417 [ 0.4405; 0.4429] 0.0 1.5 HIC
## VENEZUELA 0.8366 [ 0.8360; 0.8371] 0.6 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.0622 [4.0620; 4.0624] 53922.93 0
## Random effects model 0.3039 [0.2077; 0.4447] -6.13 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.4511 [1.7673; 5.5514]; tau = 1.5656 [1.3294; 2.3561]
## I^2 = 100.0%; H = 5831.39
##
## Test of heterogeneity:
## Q d.f. p-value
## 2176329690.77 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 5.3741 [5.3738; 5.3744] 745368761.93 100.0%
## income = UMIC 21 0.8260 [0.8259; 0.8262] 327737625.15 100.0%
## income = LMIC 6 0.0649 [0.0649; 0.0649] 3466781.07 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1099756522.62 2 0
## Within groups 1076573168.15 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.7651 [0.5442; 1.0757] 1.1482 1.0715
## income = UMIC 21 0.0921 [0.0397; 0.2138] 3.8720 1.9677
## income = LMIC 6 0.0572 [0.0390; 0.0837] 0.2274 0.4769
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 103.43 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0272 [ 0.0271; 0.0273] 0.0 1.5 UMIC
## ARGENTINA 0.1302 [ 0.1300; 0.1303] 0.1 1.5 UMIC
## AUSTRALIA 0.6512 [ 0.6507; 0.6518] 0.3 1.5 HIC
## AUSTRIA 2.1760 [ 2.1744; 2.1777] 0.4 1.5 HIC
## BELARUS 0.0476 [ 0.0474; 0.0478] 0.0 1.5 UMIC
## BELGIUM 1.0938 [ 1.0927; 1.0948] 0.3 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1982 [ 0.1974; 0.1990] 0.0 1.5 UMIC
## BRAZIL 0.1118 [ 0.1118; 0.1119] 0.5 1.5 UMIC
## BULGARIA 0.5419 [ 0.5410; 0.5428] 0.1 1.5 UMIC
## CANADA 4.1539 [ 4.1528; 4.1550] 3.3 1.5 HIC
## CHILE 0.0193 [ 0.0192; 0.0194] 0.0 1.5 HIC
## CHINA 0.0202 [ 0.0202; 0.0202] 0.6 1.5 UMIC
## COLOMBIA 0.0150 [ 0.0149; 0.0150] 0.0 1.5 UMIC
## CROATIA 0.1533 [ 0.1527; 0.1539] 0.0 1.5 HIC
## CZECH REPUBLIC 1.8848 [ 1.8834; 1.8861] 0.4 1.5 HIC
## ECUADOR 0.0448 [ 0.0447; 0.0450] 0.0 1.5 UMIC
## EGYPT 0.1340 [ 0.1339; 0.1342] 0.3 1.5 LMIC
## ESTONIA 0.7565 [ 0.7540; 0.7589] 0.0 1.5 HIC
## FINLAND 1.8835 [ 1.8816; 1.8854] 0.2 1.5 HIC
## FRANCE 1.2950 [ 1.2946; 1.2955] 1.8 1.5 HIC
## GERMANY 1.6790 [ 1.6785; 1.6794] 3.0 1.5 HIC
## GREECE 1.0152 [ 1.0142; 1.0162] 0.2 1.5 HIC
## HUNGARY 0.6659 [ 0.6651; 0.6668] 0.1 1.5 HIC
## INDIA 0.0748 [ 0.0748; 0.0749] 2.1 1.5 LMIC
## IRELAND 1.0668 [ 1.0652; 1.0684] 0.1 1.5 HIC
## ITALY 0.5614 [ 0.5610; 0.5617] 0.7 1.5 HIC
## JAPAN 0.0968 [ 0.0967; 0.0969] 0.3 1.5 HIC
## JORDAN 0.0728 [ 0.0725; 0.0731] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0261 [ 0.0259; 0.0262] 0.0 1.5 UMIC
## KUWAIT 0.1504 [ 0.1497; 0.1510] 0.0 1.5 HIC
## LATVIA 1.5928 [ 1.5899; 1.5957] 0.1 1.5 HIC
## LEBANON 0.3553 [ 0.3545; 0.3560] 0.1 1.5 UMIC
## LITHUANIA 0.3776 [ 0.3765; 0.3788] 0.0 1.5 HIC
## LUXEMBOURG 0.6946 [ 0.6910; 0.6982] 0.0 1.5 HIC
## MEXICO 0.0935 [ 0.0934; 0.0936] 0.2 1.5 UMIC
## MOROCCO 0.0421 [ 0.0420; 0.0423] 0.0 1.5 LMIC
## NETHERLANDS 0.7221 [ 0.7215; 0.7228] 0.3 1.5 HIC
## NEW ZEALAND 2.8493 [ 2.8467; 2.8518] 0.3 1.5 HIC
## NORWAY 2.6628 [ 2.6605; 2.6651] 0.3 1.5 HIC
## PAKISTAN 0.0398 [ 0.0397; 0.0398] 0.2 1.5 LMIC
## PERU 0.0558 [ 0.0557; 0.0560] 0.0 1.5 UMIC
## PHILIPPINES 0.0275 [ 0.0274; 0.0275] 0.1 1.5 LMIC
## POLAND 0.4023 [ 0.4020; 0.4027] 0.3 1.5 HIC
## PORTUGAL 1.3129 [ 1.3117; 1.3140] 0.3 1.5 HIC
## PUERTO RICO 11.7963 [11.7902; 11.8024] 0.9 1.5 HIC
## ROMANIA 0.9553 [ 0.9545; 0.9560] 0.4 1.5 UMIC
## RUSSIA 0.0453 [ 0.0453; 0.0454] 0.1 1.5 UMIC
## SAUDI ARABIA 0.2044 [ 0.2042; 0.2047] 0.1 1.5 HIC
## SERBIA 0.1135 [ 0.1131; 0.1138] 0.0 1.5 UMIC
## SLOVAKIA 1.5663 [ 1.5645; 1.5680] 0.2 1.5 HIC
## SLOVENIA 0.3335 [ 0.3322; 0.3348] 0.0 1.5 HIC
## SOUTH AFRICA 0.0551 [ 0.0550; 0.0552] 0.1 1.5 UMIC
## SOUTH KOREA 0.8380 [ 0.8375; 0.8384] 0.9 1.5 HIC
## SPAIN 1.9664 [ 1.9657; 1.9670] 2.0 1.5 HIC
## SWEDEN 2.7401 [ 2.7384; 2.7418] 0.6 1.5 HIC
## SWITZERLAND 0.5081 [ 0.5073; 0.5089] 0.1 1.5 HIC
## TAIWAN 0.2013 [ 0.2010; 0.2016] 0.1 1.5 HIC
## THAILAND 0.5087 [ 0.5084; 0.5090] 0.8 1.5 UMIC
## TUNISIA 0.0958 [ 0.0955; 0.0961] 0.0 1.5 LMIC
## TÜRKIYE 2.6150 [ 2.6144; 2.6156] 4.5 1.5 UMIC
## UNITED ARAB EMIRATES 0.1007 [ 0.1004; 0.1011] 0.0 1.5 HIC
## UNITED KINGDOM 5.1176 [ 5.1167; 5.1185] 7.3 1.5 HIC
## UNITED STATES 9.2026 [ 9.2020; 9.2031] 64.3 1.5 HIC
## URUGUAY 0.3638 [ 0.3627; 0.3649] 0.0 1.5 HIC
## VENEZUELA 0.3521 [ 0.3517; 0.3524] 0.2 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.7307 [4.7305; 4.7309] 63664.08 0
## Random effects model 0.3411 [0.2293; 0.5074] -5.31 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.6697 [1.8264; 5.7894]; tau = 1.6339 [1.3515; 2.4061]
## I^2 = 100.0%; H = 6349.46
##
## Test of heterogeneity:
## Q d.f. p-value
## 2580199141.57 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 6.2883 [6.2879; 6.2886] 880698476.98 100.0%
## income = UMIC 21 0.7719 [0.7717; 0.7720] 359514883.27 100.0%
## income = LMIC 6 0.0741 [0.0741; 0.0741] 3918468.23 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1336067313.09 2 0
## Within groups 1244131828.48 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.8287 [0.5816; 1.1808] 1.2402 1.1137
## income = UMIC 21 0.1127 [0.0470; 0.2699] 4.1734 2.0429
## income = LMIC 6 0.0595 [0.0401; 0.0882] 0.2429 0.4929
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 98.00 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0677 [ 0.0676; 0.0678] 0.1 1.5 UMIC
## ARGENTINA 0.1252 [ 0.1251; 0.1254] 0.1 1.5 UMIC
## AUSTRALIA 0.6366 [ 0.6361; 0.6371] 0.3 1.5 HIC
## AUSTRIA 2.1089 [ 2.1074; 2.1105] 0.4 1.5 HIC
## BELARUS 0.0535 [ 0.0532; 0.0537] 0.0 1.5 UMIC
## BELGIUM 0.9712 [ 0.9703; 0.9722] 0.2 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.2626 [ 0.2617; 0.2635] 0.0 1.5 UMIC
## BRAZIL 0.1122 [ 0.1121; 0.1123] 0.5 1.5 UMIC
## BULGARIA 0.5557 [ 0.5548; 0.5566] 0.1 1.5 UMIC
## CANADA 4.5476 [ 4.5464; 4.5487] 3.3 1.5 HIC
## CHILE 0.0195 [ 0.0194; 0.0196] 0.0 1.5 HIC
## CHINA 0.0238 [ 0.0238; 0.0238] 0.7 1.5 UMIC
## COLOMBIA 0.0147 [ 0.0147; 0.0148] 0.0 1.5 UMIC
## CROATIA 0.1439 [ 0.1433; 0.1445] 0.0 1.5 HIC
## CZECH REPUBLIC 1.9731 [ 1.9717; 1.9745] 0.4 1.5 HIC
## ECUADOR 0.0484 [ 0.0483; 0.0486] 0.0 1.5 UMIC
## EGYPT 0.1568 [ 0.1567; 0.1570] 0.3 1.5 LMIC
## ESTONIA 0.8935 [ 0.8908; 0.8961] 0.0 1.5 HIC
## FINLAND 2.2200 [ 2.2180; 2.2221] 0.2 1.5 HIC
## FRANCE 1.3220 [ 1.3215; 1.3224] 1.7 1.5 HIC
## GERMANY 1.6877 [ 1.6872; 1.6881] 2.7 1.5 HIC
## GREECE 1.0272 [ 1.0262; 1.0283] 0.2 1.5 HIC
## HUNGARY 0.7149 [ 0.7140; 0.7157] 0.1 1.5 HIC
## INDIA 0.0872 [ 0.0871; 0.0872] 2.3 1.5 LMIC
## IRELAND 1.1315 [ 1.1300; 1.1331] 0.1 1.5 HIC
## ITALY 0.5573 [ 0.5569; 0.5576] 0.7 1.5 HIC
## JAPAN 0.0910 [ 0.0909; 0.0910] 0.2 1.5 HIC
## JORDAN 0.0856 [ 0.0852; 0.0859] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0254 [ 0.0253; 0.0255] 0.0 1.5 UMIC
## KUWAIT 0.4524 [ 0.4513; 0.4535] 0.0 1.5 HIC
## LATVIA 1.7258 [ 1.7228; 1.7288] 0.1 1.5 HIC
## LEBANON 0.4366 [ 0.4357; 0.4374] 0.1 1.5 UMIC
## LITHUANIA 0.4294 [ 0.4282; 0.4307] 0.0 1.5 HIC
## LUXEMBOURG 0.6676 [ 0.6641; 0.6710] 0.0 1.5 HIC
## MEXICO 0.1015 [ 0.1014; 0.1016] 0.2 1.5 UMIC
## MOROCCO 0.0514 [ 0.0512; 0.0515] 0.0 1.5 LMIC
## NETHERLANDS 0.7640 [ 0.7633; 0.7647] 0.3 1.5 HIC
## NEW ZEALAND 3.3486 [ 3.3459; 3.3514] 0.3 1.5 HIC
## NORWAY 2.7479 [ 2.7456; 2.7503] 0.3 1.5 HIC
## PAKISTAN 0.0364 [ 0.0364; 0.0365] 0.1 1.5 LMIC
## PERU 0.0566 [ 0.0565; 0.0568] 0.0 1.5 UMIC
## PHILIPPINES 0.0275 [ 0.0274; 0.0275] 0.1 1.5 LMIC
## POLAND 0.4448 [ 0.4445; 0.4452] 0.3 1.5 HIC
## PORTUGAL 1.3466 [ 1.3455; 1.3478] 0.3 1.5 HIC
## PUERTO RICO 13.2279 [13.2214; 13.2344] 0.9 1.5 HIC
## ROMANIA 1.1118 [ 1.1110; 1.1125] 0.4 1.5 UMIC
## RUSSIA 0.0930 [ 0.0930; 0.0931] 0.3 1.5 UMIC
## SAUDI ARABIA 0.4083 [ 0.4079; 0.4087] 0.3 1.5 HIC
## SERBIA 0.1056 [ 0.1052; 0.1060] 0.0 1.5 UMIC
## SLOVAKIA 1.4980 [ 1.4963; 1.4997] 0.2 1.5 HIC
## SLOVENIA 0.3248 [ 0.3235; 0.3261] 0.0 1.5 HIC
## SOUTH AFRICA 0.0642 [ 0.0640; 0.0643] 0.1 1.5 UMIC
## SOUTH KOREA 0.8851 [ 0.8847; 0.8855] 0.9 1.5 HIC
## SPAIN 2.0645 [ 2.0638; 2.0652] 1.9 1.5 HIC
## SWEDEN 3.4050 [ 3.4031; 3.4069] 0.7 1.5 HIC
## SWITZERLAND 0.5020 [ 0.5012; 0.5028] 0.1 1.5 HIC
## TAIWAN 0.2339 [ 0.2335; 0.2342] 0.1 1.5 HIC
## THAILAND 0.5326 [ 0.5324; 0.5329] 0.7 1.5 UMIC
## TUNISIA 0.0939 [ 0.0936; 0.0942] 0.0 1.5 LMIC
## TÜRKIYE 2.4956 [ 2.4950; 2.4961] 3.9 1.5 UMIC
## UNITED ARAB EMIRATES 0.0726 [ 0.0723; 0.0729] 0.0 1.5 HIC
## UNITED KINGDOM 5.7608 [ 5.7598; 5.7617] 7.5 1.5 HIC
## UNITED STATES 10.2970 [10.2964; 10.2975] 65.3 1.5 HIC
## URUGUAY 0.3608 [ 0.3598; 0.3619] 0.0 1.5 HIC
## VENEZUELA 0.0491 [ 0.0490; 0.0493] 0.0 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.2903 [5.2900; 5.2905] 71831.16 0
## Random effects model 0.3651 [0.2427; 0.5491] -4.84 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.8207 [1.8569; 5.9271]; tau = 1.6795 [1.3627; 2.4346]
## I^2 = 100.0%; H = 6788.80
##
## Test of heterogeneity:
## Q d.f. p-value
## 2949619479.48 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 7.0825 [7.0821; 7.0828] 996261547.11 100.0%
## income = UMIC 21 0.6975 [0.6974; 0.6976] 365222609.20 100.0%
## income = LMIC 6 0.0860 [0.0860; 0.0860] 5490788.01 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1582644535.16 2 0
## Within groups 1366974944.32 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.8986 [0.6263; 1.2893] 1.2893 1.1355
## income = UMIC 21 0.1179 [0.0500; 0.2776] 4.0114 2.0029
## income = LMIC 6 0.0636 [0.0407; 0.0993] 0.3103 0.5571
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 86.47 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.1275 [ 0.1273; 0.1277] 0.1 1.5 UMIC
## ARGENTINA 0.1241 [ 0.1240; 0.1243] 0.1 1.5 UMIC
## AUSTRALIA 0.6583 [ 0.6578; 0.6589] 0.3 1.5 HIC
## AUSTRIA 2.0880 [ 2.0864; 2.0896] 0.3 1.5 HIC
## BELARUS 0.0843 [ 0.0840; 0.0846] 0.0 1.5 UMIC
## BELGIUM 0.9062 [ 0.9053; 0.9071] 0.2 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.3009 [ 0.3000; 0.3019] 0.0 1.5 UMIC
## BRAZIL 0.1148 [ 0.1148; 0.1149] 0.4 1.5 UMIC
## BULGARIA 0.5224 [ 0.5215; 0.5233] 0.1 1.5 UMIC
## CANADA 4.7693 [ 4.7681; 4.7705] 3.1 1.5 HIC
## CHILE 0.0198 [ 0.0197; 0.0200] 0.0 1.5 HIC
## CHINA 0.0293 [ 0.0293; 0.0293] 0.7 1.5 UMIC
## COLOMBIA 0.0119 [ 0.0118; 0.0119] 0.0 1.5 UMIC
## CROATIA 0.1282 [ 0.1276; 0.1288] 0.0 1.5 HIC
## CZECH REPUBLIC 2.0591 [ 2.0577; 2.0606] 0.4 1.5 HIC
## ECUADOR 0.0520 [ 0.0519; 0.0522] 0.0 1.5 UMIC
## EGYPT 0.1489 [ 0.1488; 0.1491] 0.3 1.5 LMIC
## ESTONIA 1.1053 [ 1.1024; 1.1083] 0.0 1.5 HIC
## FINLAND 2.6654 [ 2.6631; 2.6676] 0.3 1.5 HIC
## FRANCE 1.3419 [ 1.3415; 1.3424] 1.5 1.5 HIC
## GERMANY 1.6769 [ 1.6765; 1.6774] 2.5 1.5 HIC
## GREECE 1.0026 [ 1.0016; 1.0036] 0.2 1.5 HIC
## HUNGARY 0.7477 [ 0.7468; 0.7486] 0.1 1.5 HIC
## INDIA 0.0989 [ 0.0989; 0.0989] 2.4 1.5 LMIC
## IRELAND 1.1919 [ 1.1903; 1.1936] 0.1 1.5 HIC
## ITALY 0.5694 [ 0.5691; 0.5698] 0.6 1.5 HIC
## JAPAN 0.0856 [ 0.0855; 0.0857] 0.2 1.5 HIC
## JORDAN 0.1192 [ 0.1189; 0.1196] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0230 [ 0.0229; 0.0231] 0.0 1.5 UMIC
## KUWAIT 0.9078 [ 0.9062; 0.9093] 0.1 1.5 HIC
## LATVIA 2.0008 [ 1.9975; 2.0041] 0.1 1.5 HIC
## LEBANON 0.4970 [ 0.4962; 0.4979] 0.1 1.5 UMIC
## LITHUANIA 0.5211 [ 0.5197; 0.5225] 0.0 1.5 HIC
## LUXEMBOURG 0.6295 [ 0.6261; 0.6328] 0.0 1.5 HIC
## MEXICO 0.0994 [ 0.0993; 0.0995] 0.2 1.5 UMIC
## MOROCCO 0.0369 [ 0.0368; 0.0370] 0.0 1.5 LMIC
## NETHERLANDS 0.7887 [ 0.7880; 0.7894] 0.2 1.5 HIC
## NEW ZEALAND 3.7529 [ 3.7500; 3.7558] 0.3 1.5 HIC
## NORWAY 2.9508 [ 2.9484; 2.9532] 0.3 1.5 HIC
## PAKISTAN 0.0311 [ 0.0310; 0.0311] 0.1 1.5 LMIC
## PERU 0.0733 [ 0.0732; 0.0735] 0.0 1.5 UMIC
## PHILIPPINES 0.0243 [ 0.0242; 0.0243] 0.0 1.5 LMIC
## POLAND 0.4682 [ 0.4678; 0.4685] 0.3 1.5 HIC
## PORTUGAL 1.4084 [ 1.4072; 1.4096] 0.3 1.5 HIC
## PUERTO RICO 14.8281 [14.8211; 14.8351] 0.8 1.5 HIC
## ROMANIA 1.3080 [ 1.3072; 1.3089] 0.5 1.5 UMIC
## RUSSIA 0.1532 [ 0.1531; 0.1533] 0.4 1.5 UMIC
## SAUDI ARABIA 0.5669 [ 0.5665; 0.5673] 0.3 1.5 HIC
## SERBIA 0.1059 [ 0.1055; 0.1062] 0.0 1.5 UMIC
## SLOVAKIA 1.5627 [ 1.5609; 1.5644] 0.2 1.5 HIC
## SLOVENIA 0.3402 [ 0.3389; 0.3415] 0.0 1.5 HIC
## SOUTH AFRICA 0.0655 [ 0.0654; 0.0656] 0.1 1.5 UMIC
## SOUTH KOREA 0.9343 [ 0.9339; 0.9348] 0.9 1.5 HIC
## SPAIN 2.1655 [ 2.1648; 2.1662] 1.8 1.5 HIC
## SWEDEN 3.9049 [ 3.9029; 3.9070] 0.7 1.5 HIC
## SWITZERLAND 0.5067 [ 0.5059; 0.5075] 0.1 1.5 HIC
## TAIWAN 0.2404 [ 0.2401; 0.2407] 0.1 1.5 HIC
## THAILAND 0.6236 [ 0.6233; 0.6239] 0.8 1.5 UMIC
## TUNISIA 0.0915 [ 0.0912; 0.0918] 0.0 1.5 LMIC
## TÜRKIYE 2.5773 [ 2.5767; 2.5779] 3.7 1.5 UMIC
## UNITED ARAB EMIRATES 0.0593 [ 0.0590; 0.0595] 0.0 1.5 HIC
## UNITED KINGDOM 6.1083 [ 6.1073; 6.1093] 7.3 1.5 HIC
## UNITED STATES 11.4514 [11.4508; 11.4520] 66.3 1.5 HIC
## URUGUAY 0.3376 [ 0.3365; 0.3386] 0.0 1.5 HIC
## VENEZUELA 0.0634 [ 0.0633; 0.0636] 0.0 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.8461 [5.8459; 5.8464] 79960.47 0
## Random effects model 0.3929 [0.2587; 0.5968] -4.38 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.9569 [1.8636; 5.9664]; tau = 1.7196 [1.3651; 2.4426]
## I^2 = 100.0%; H = 7221.33
##
## Test of heterogeneity:
## Q d.f. p-value
## 3337446034.08 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 7.9048 [7.9044; 7.9051] 1115960133.86 100.0%
## income = UMIC 21 0.6977 [0.6976; 0.6978] 390529505.51 100.0%
## income = LMIC 6 0.0949 [0.0949; 0.0949] 6252345.79 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1824704048.92 2 0
## Within groups 1512741985.16 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.9567 [0.6606; 1.3855] 1.3568 1.1648
## income = UMIC 21 0.1357 [0.0596; 0.3089] 3.6961 1.9225
## income = LMIC 6 0.0579 [0.0354; 0.0944] 0.3750 0.6123
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 84.52 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0899 [ 0.0898; 0.0901] 0.1 1.5 UMIC
## ARGENTINA 0.1258 [ 0.1257; 0.1260] 0.1 1.5 UMIC
## AUSTRALIA 0.7003 [ 0.6998; 0.7009] 0.3 1.5 HIC
## AUSTRIA 2.0631 [ 2.0615; 2.0646] 0.3 1.5 HIC
## BELARUS 0.1204 [ 0.1200; 0.1208] 0.0 1.5 UMIC
## BELGIUM 0.8770 [ 0.8761; 0.8779] 0.2 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.3650 [ 0.3640; 0.3661] 0.0 1.5 UMIC
## BRAZIL 0.1201 [ 0.1201; 0.1202] 0.4 1.5 UMIC
## BULGARIA 0.4317 [ 0.4309; 0.4325] 0.1 1.5 UMIC
## CANADA 4.8245 [ 4.8233; 4.8256] 3.1 1.5 HIC
## CHILE 0.0214 [ 0.0213; 0.0215] 0.0 1.5 HIC
## CHINA 0.0330 [ 0.0329; 0.0330] 0.8 1.5 UMIC
## COLOMBIA 0.0146 [ 0.0145; 0.0146] 0.0 1.5 UMIC
## CROATIA 0.1217 [ 0.1211; 0.1223] 0.0 1.5 HIC
## CZECH REPUBLIC 2.0989 [ 2.0974; 2.1003] 0.4 1.5 HIC
## ECUADOR 0.0512 [ 0.0510; 0.0513] 0.0 1.5 UMIC
## EGYPT 0.1647 [ 0.1646; 0.1648] 0.3 1.5 LMIC
## ESTONIA 1.2831 [ 1.2799; 1.2862] 0.0 1.5 HIC
## FINLAND 3.0971 [ 3.0947; 3.0996] 0.3 1.5 HIC
## FRANCE 1.3516 [ 1.3511; 1.3521] 1.5 1.5 HIC
## GERMANY 1.6751 [ 1.6746; 1.6755] 2.4 1.5 HIC
## GREECE 0.9888 [ 0.9878; 0.9898] 0.2 1.5 HIC
## HUNGARY 0.8107 [ 0.8098; 0.8117] 0.1 1.5 HIC
## INDIA 0.1112 [ 0.1112; 0.1112] 2.6 1.5 LMIC
## IRELAND 1.2545 [ 1.2528; 1.2562] 0.1 1.5 HIC
## ITALY 0.5734 [ 0.5731; 0.5737] 0.6 1.5 HIC
## JAPAN 0.0833 [ 0.0832; 0.0833] 0.2 1.5 HIC
## JORDAN 0.3329 [ 0.3323; 0.3335] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0263 [ 0.0262; 0.0265] 0.0 1.5 UMIC
## KUWAIT 0.9165 [ 0.9150; 0.9180] 0.1 1.5 HIC
## LATVIA 2.3533 [ 2.3498; 2.3569] 0.1 1.5 HIC
## LEBANON 0.4987 [ 0.4978; 0.4996] 0.1 1.5 UMIC
## LITHUANIA 0.5957 [ 0.5942; 0.5972] 0.0 1.5 HIC
## LUXEMBOURG 0.6434 [ 0.6400; 0.6467] 0.0 1.5 HIC
## MEXICO 0.1003 [ 0.1002; 0.1004] 0.2 1.5 UMIC
## MOROCCO 0.0402 [ 0.0401; 0.0403] 0.0 1.5 LMIC
## NETHERLANDS 0.8129 [ 0.8122; 0.8136] 0.2 1.5 HIC
## NEW ZEALAND 3.9910 [ 3.9880; 3.9939] 0.3 1.5 HIC
## NORWAY 3.1948 [ 3.1923; 3.1973] 0.3 1.5 HIC
## PAKISTAN 0.0328 [ 0.0328; 0.0328] 0.1 1.5 LMIC
## PERU 0.0703 [ 0.0701; 0.0704] 0.0 1.5 UMIC
## PHILIPPINES 0.0299 [ 0.0298; 0.0299] 0.1 1.5 LMIC
## POLAND 0.5082 [ 0.5079; 0.5086] 0.3 1.5 HIC
## PORTUGAL 1.4806 [ 1.4794; 1.4819] 0.3 1.5 HIC
## PUERTO RICO 13.9247 [13.9178; 13.9317] 0.7 1.5 HIC
## ROMANIA 1.3918 [ 1.3909; 1.3926] 0.5 1.5 UMIC
## RUSSIA 0.2102 [ 0.2101; 0.2103] 0.5 1.5 UMIC
## SAUDI ARABIA 0.7810 [ 0.7805; 0.7815] 0.5 1.5 HIC
## SERBIA 0.1066 [ 0.1063; 0.1070] 0.0 1.5 UMIC
## SLOVAKIA 1.7118 [ 1.7100; 1.7136] 0.2 1.5 HIC
## SLOVENIA 0.3713 [ 0.3700; 0.3727] 0.0 1.5 HIC
## SOUTH AFRICA 0.0662 [ 0.0661; 0.0663] 0.1 1.5 UMIC
## SOUTH KOREA 0.9307 [ 0.9303; 0.9312] 0.8 1.5 HIC
## SPAIN 2.2219 [ 2.2212; 2.2226] 1.8 1.5 HIC
## SWEDEN 4.4354 [ 4.4332; 4.4376] 0.8 1.5 HIC
## SWITZERLAND 0.5148 [ 0.5141; 0.5156] 0.1 1.5 HIC
## TAIWAN 0.2588 [ 0.2585; 0.2592] 0.1 1.5 HIC
## THAILAND 0.7063 [ 0.7060; 0.7067] 0.8 1.5 UMIC
## TUNISIA 0.0909 [ 0.0906; 0.0911] 0.0 1.5 LMIC
## TÜRKIYE 2.5175 [ 2.5170; 2.5181] 3.6 1.5 UMIC
## UNITED ARAB EMIRATES 0.0566 [ 0.0563; 0.0568] 0.0 1.5 HIC
## UNITED KINGDOM 6.1017 [ 6.1008; 6.1027] 7.1 1.5 HIC
## UNITED STATES 11.6873 [11.6867; 11.6879] 66.1 1.5 HIC
## URUGUAY 0.3050 [ 0.3040; 0.3059] 0.0 1.5 HIC
## VENEZUELA 0.0258 [ 0.0257; 0.0259] 0.0 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.8486 [5.8484; 5.8489] 81200.80 0
## Random effects model 0.4135 [0.2713; 0.6301] -4.11 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 3.0022 [1.8266; 5.8085]; tau = 1.7327 [1.3515; 2.4101]
## I^2 = 100.0%; H = 7411.30
##
## Test of heterogeneity:
## Q d.f. p-value
## 3515352636.41 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 8.0400 [8.0397; 8.0404] 1142563271.88 100.0%
## income = UMIC 21 0.6752 [0.6751; 0.6753] 389938858.93 100.0%
## income = LMIC 6 0.1064 [0.1063; 0.1064] 7146336.44 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1975704169.16 2 0
## Within groups 1539648467.25 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.9992 [0.6900; 1.4469] 1.3563 1.1646
## income = UMIC 21 0.1431 [0.0650; 0.3149] 3.4022 1.8445
## income = LMIC 6 0.0635 [0.0387; 0.1041] 0.3817 0.6178
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 81.39 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0000 0.0 0.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0000 0.0 0.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 0
##
## rate 95%-CI z p-value
## Common effect model NA -- --
## Random effects model NA -- --
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups NA 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2009 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0000 0.0 0.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0000 0.0 0.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 0
##
## rate 95%-CI z p-value
## Common effect model NA -- --
## Random effects model NA -- --
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups NA 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2010 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0000 0.0 0.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0000 0.0 0.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 0
##
## rate 95%-CI z p-value
## Common effect model NA -- --
## Random effects model NA -- --
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups NA 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2011 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0000 0.0 0.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0039 [0.0039; 0.0039] 100.0 100.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 1
##
## rate 95%-CI z p-value
## Common effect model 0.0039 [0.0039; 0.0039] -3694.30 0
## Random effects model 0.0039 [0.0039; 0.0039] -3694.30 0
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 1 0.0039 [0.0039; 0.0039] 0.00 --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 1 0.0039 [0.0039; 0.0039] -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2012 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0094 [0.0094; 0.0095] 22.4 35.7 HIC
## PUERTO RICO 0.0001 [0.0000; 0.0001] 0.0 28.6 HIC
## UNITED STATES 0.0133 [0.0133; 0.0134] 77.6 35.7 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 3
##
## rate 95%-CI z p-value
## Common effect model 0.0124 [0.0123; 0.0124] -6174.23 0
## Random effects model 0.0024 [0.0018; 0.0032] -40.32 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0626 [0.0659; 53.3307]; tau = 0.2503 [0.2567; 7.3028]
## I^2 = 100.0%; H = 146.72
##
## Test of heterogeneity:
## Q d.f. p-value
## 43054.43 2 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 3 0.0124 [0.0123; 0.0124] 43054.43 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 43054.43 2 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 3 0.0024 [0.0018; 0.0032] 0.0626 0.2503
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0313 [0.0313; 0.0314] 50.9 33.5 HIC
## PUERTO RICO 0.0001 [0.0001; 0.0001] 0.0 33.0 HIC
## UNITED STATES 0.0122 [0.0122; 0.0123] 49.1 33.5 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 3
##
## rate 95%-CI z p-value
## Common effect model 0.0198 [0.0197; 0.0198] -6663.61 0
## Random effects model 0.0037 [0.0017; 0.0078] -14.53 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.4448 [0.1404; >100.0000]; tau = 0.6669 [0.3747; >10.0000]
## I^2 = 100.0%; H = 566.14
##
## Test of heterogeneity:
## Q d.f. p-value
## 641032.17 2 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 3 0.0198 [0.0197; 0.0198] 641032.17 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 641032.17 2 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 3 0.0037 [0.0017; 0.0078] 0.4448 0.6669
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0440 [0.0439; 0.0441] 44.0 50.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0225 [0.0225; 0.0226] 56.0 50.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0302 [0.0302; 0.0303] -7570.26 0
## Random effects model 0.0315 [0.0163; 0.0607] -10.34 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.2239; tau = 0.4731; I^2 = 100.0%; H = 718.72
##
## Test of heterogeneity:
## Q d.f. p-value
## 516556.17 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 2 0.0302 [0.0302; 0.0303] 516556.17 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 516556.17 1 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 2 0.0315 [0.0163; 0.0607] 0.2239 0.4731
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2015 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0502 [0.0502; 0.0503] 36.2 35.1 HIC
## PUERTO RICO 0.0001 [0.0001; 0.0001] 0.0 29.8 HIC
## UNITED STATES 0.0353 [0.0353; 0.0353] 63.8 35.1 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 3
##
## rate 95%-CI z p-value
## Common effect model 0.0401 [0.0401; 0.0401] -8191.00 0
## Random effects model 0.0063 [0.0047; 0.0085] -33.91 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.0634 [0.0549; 57.4238]; tau = 0.2518 [0.2343; 7.5778]
## I^2 = 100.0%; H = 308.20
##
## Test of heterogeneity:
## Q d.f. p-value
## 189978.76 2 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 3 0.0401 [0.0401; 0.0401] 189978.76 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 189978.76 2 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 3 0.0063 [0.0047; 0.0085] 0.0634 0.2518
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0542 [0.0542; 0.0543] 29.8 50.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0505 [0.0505; 0.0506] 70.2 50.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0516 [0.0516; 0.0516] -8638.01 0
## Random effects model 0.0523 [0.0489; 0.0561] -83.96 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0025; tau = 0.0497; I^2 = 100.0%; H = 93.66
##
## Test of heterogeneity:
## Q d.f. p-value
## 8771.96 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 2 0.0516 [0.0516; 0.0516] 8771.96 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 8771.96 1 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 2 0.0523 [0.0489; 0.0561] 0.0025 0.0497
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2017 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0581 [0.0580; 0.0582] 29.7 50.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0540 [0.0539; 0.0540] 70.3 50.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0552 [0.0551; 0.0552] -8747.31 0
## Random effects model 0.0560 [0.0521; 0.0602] -78.44 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0027; tau = 0.0520; I^2 = 100.0%; H = 101.38
##
## Test of heterogeneity:
## Q d.f. p-value
## 10276.94 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 2 0.0552 [0.0551; 0.0552] 10276.94 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 10276.94 1 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 2 0.0560 [0.0521; 0.0602] 0.0027 0.0520
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2018 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0635 [0.0634; 0.0636] 34.4 50.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0470 [0.0470; 0.0471] 65.6 50.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0521 [0.0521; 0.0522] -8645.90 0
## Random effects model 0.0546 [0.0407; 0.0733] -19.39 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.0450; tau = 0.2121; I^2 = 100.0%; H = 417.10
##
## Test of heterogeneity:
## Q d.f. p-value
## 173973.45 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 2 0.0521 [0.0521; 0.0522] 173973.45 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 173973.45 1 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 2 0.0546 [0.0407; 0.0733] 0.0450 0.2121
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2008 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0709 [0.0708; 0.0711] 0.2 1.8 UMIC
## ARGENTINA 0.1063 [0.1061; 0.1065] 0.3 1.8 UMIC
## AUSTRALIA 0.5278 [0.5272; 0.5283] 0.7 1.8 HIC
## AUSTRIA 0.7538 [0.7528; 0.7548] 0.4 1.8 HIC
## BELARUS 0.0007 [0.0007; 0.0008] 0.0 1.8 UMIC
## BELGIUM 0.9523 [0.9513; 0.9533] 0.7 1.8 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0186 [0.0185; 0.0188] 0.0 1.8 UMIC
## CANADA 1.6387 [1.6380; 1.6395] 3.5 1.8 HIC
## CHILE 0.1689 [0.1685; 0.1692] 0.2 1.8 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0536 [0.0535; 0.0537] 0.2 1.8 UMIC
## CROATIA 0.0233 [0.0231; 0.0236] 0.0 1.8 HIC
## CZECH REPUBLIC 0.4263 [0.4256; 0.4269] 0.3 1.8 HIC
## ECUADOR 0.1220 [0.1217; 0.1223] 0.1 1.8 UMIC
## EGYPT 0.0305 [0.0305; 0.0306] 0.2 1.8 LMIC
## ESTONIA 0.1266 [0.1256; 0.1276] 0.0 1.8 HIC
## FINLAND 3.3965 [3.3939; 3.3991] 1.1 1.8 HIC
## FRANCE 2.1673 [2.1667; 2.1679] 8.6 1.8 HIC
## GERMANY 1.5554 [1.5549; 1.5558] 8.0 1.8 HIC
## GREECE 1.4149 [1.4138; 1.4161] 1.0 1.8 HIC
## HUNGARY 0.4953 [0.4946; 0.4961] 0.3 1.8 HIC
## INDIA 0.0834 [0.0834; 0.0835] 6.4 1.8 LMIC
## IRELAND 1.9534 [1.9512; 1.9556] 0.5 1.8 HIC
## ITALY 0.9209 [0.9205; 0.9213] 3.4 1.8 HIC
## JAPAN 0.0000 0.0 0.0 HIC
## JORDAN 0.0510 [0.0507; 0.0513] 0.0 1.8 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0877 [0.0871; 0.0883] 0.0 1.8 HIC
## LATVIA 0.0843 [0.0837; 0.0849] 0.0 1.8 HIC
## LEBANON 0.1605 [0.1599; 0.1611] 0.0 1.8 UMIC
## LITHUANIA 0.1282 [0.1276; 0.1289] 0.0 1.8 HIC
## LUXEMBOURG 2.3963 [2.3891; 2.4035] 0.1 1.8 HIC
## MEXICO 0.2075 [0.2074; 0.2077] 1.5 1.8 UMIC
## MOROCCO 0.0162 [0.0161; 0.0163] 0.0 1.8 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0120 [0.0118; 0.0121] 0.0 1.8 HIC
## NORWAY 2.3244 [2.3221; 2.3267] 0.7 1.8 HIC
## PAKISTAN 0.0431 [0.0430; 0.0431] 0.5 1.8 LMIC
## PERU 0.0255 [0.0254; 0.0256] 0.0 1.8 UMIC
## PHILIPPINES 0.0270 [0.0269; 0.0270] 0.2 1.8 LMIC
## POLAND 0.0043 [0.0043; 0.0043] 0.0 1.8 HIC
## PORTUGAL 1.8127 [1.8114; 1.8140] 1.2 1.8 HIC
## PUERTO RICO 1.2632 [1.2613; 1.2651] 0.3 1.8 HIC
## ROMANIA 0.2512 [0.2508; 0.2515] 0.3 1.8 UMIC
## RUSSIA 0.0137 [0.0137; 0.0138] 0.1 1.8 UMIC
## SAUDI ARABIA 0.2557 [0.2553; 0.2560] 0.4 1.8 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.8130 [0.8117; 0.8143] 0.3 1.8 HIC
## SLOVENIA 1.0760 [1.0736; 1.0784] 0.1 1.8 HIC
## SOUTH AFRICA 0.0644 [0.0643; 0.0645] 0.2 1.8 UMIC
## SOUTH KOREA 0.3052 [0.3049; 0.3055] 1.0 1.8 HIC
## SPAIN 2.0490 [2.0484; 2.0497] 6.0 1.8 HIC
## SWEDEN 2.1903 [2.1887; 2.1919] 1.3 1.8 HIC
## SWITZERLAND 1.3323 [1.3310; 1.3337] 0.6 1.8 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0578 [0.0575; 0.0580] 0.0 1.8 LMIC
## TÜRKIYE 0.2052 [0.2050; 0.2053] 0.9 1.8 UMIC
## UNITED ARAB EMIRATES 0.2101 [0.2095; 0.2106] 0.1 1.8 HIC
## UNITED KINGDOM 1.3586 [1.3581; 1.3591] 5.4 1.8 HIC
## UNITED STATES 2.1871 [2.1868; 2.1873] 42.2 1.8 HIC
## URUGUAY 0.0582 [0.0578; 0.0586] 0.0 1.8 HIC
## VENEZUELA 0.2297 [0.2294; 0.2300] 0.4 1.8 UMIC
##
## Number of studies combined: k = 56
##
## rate 95%-CI z p-value
## Common effect model 1.2863 [1.2862; 1.2864] 6034.53 0
## Random effects model 0.2112 [0.1564; 0.2852] -10.15 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.3158 [1.2226; 3.7999]; tau = 1.1471 [1.1057; 1.9493]
## I^2 = 100.0%; H = 3303.27
##
## Test of heterogeneity:
## Q d.f. p-value
## 600138502.72 55 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 35 1.7950 [1.7948; 1.7951] 87012755.52 100.0%
## income = UMIC 15 0.1544 [0.1543; 0.1544] 10163892.16 100.0%
## income = LMIC 6 0.0756 [0.0756; 0.0756] 3350072.95 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 499611782.09 2 0
## Within groups 100526720.63 53 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 35 0.4929 [0.4204; 0.5779] 0.2304 0.4800
## income = UMIC 15 0.0583 [0.0406; 0.0838] 0.5124 0.7158
## income = LMIC 6 0.0375 [0.0230; 0.0611] 0.3727 0.6105
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 185.39 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.2753 [0.2750; 0.2756] 0.5 1.8 UMIC
## ARGENTINA 0.1721 [0.1719; 0.1723] 0.4 1.8 UMIC
## AUSTRALIA 0.7022 [0.7016; 0.7028] 0.8 1.8 HIC
## AUSTRIA 0.9630 [0.9619; 0.9641] 0.4 1.8 HIC
## BELARUS 0.0048 [0.0047; 0.0049] 0.0 1.8 UMIC
## BELGIUM 1.2113 [1.2103; 1.2124] 0.7 1.8 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0149 [0.0149; 0.0149] 0.2 1.8 UMIC
## BULGARIA 0.1057 [0.1053; 0.1061] 0.0 1.8 UMIC
## CANADA 2.1523 [2.1515; 2.1531] 3.9 1.8 HIC
## CHILE 0.1928 [0.1924; 0.1931] 0.2 1.8 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0553 [0.0552; 0.0554] 0.1 1.8 UMIC
## CROATIA 0.0632 [0.0628; 0.0636] 0.0 1.8 HIC
## CZECH REPUBLIC 0.5902 [0.5894; 0.5909] 0.3 1.8 HIC
## ECUADOR 0.1724 [0.1721; 0.1728] 0.1 1.8 UMIC
## EGYPT 0.0666 [0.0665; 0.0667] 0.3 1.8 LMIC
## ESTONIA 0.1453 [0.1442; 0.1463] 0.0 1.8 HIC
## FINLAND 4.0030 [4.0002; 4.0058] 1.1 1.8 HIC
## FRANCE 2.5553 [2.5547; 2.5560] 8.6 1.8 HIC
## GERMANY 1.8744 [1.8739; 1.8749] 8.1 1.8 HIC
## GREECE 1.8254 [1.8241; 1.8267] 1.1 1.8 HIC
## HUNGARY 0.5623 [0.5615; 0.5631] 0.3 1.8 HIC
## INDIA 0.1226 [0.1226; 0.1226] 8.0 1.8 LMIC
## IRELAND 2.6032 [2.6008; 2.6057] 0.6 1.8 HIC
## ITALY 1.1183 [1.1179; 1.1188] 3.5 1.8 HIC
## JAPAN 0.0000 0.0 0.0 HIC
## JORDAN 0.0665 [0.0662; 0.0669] 0.0 1.8 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1249 [0.1242; 0.1256] 0.0 1.8 HIC
## LATVIA 0.1286 [0.1278; 0.1294] 0.0 1.8 HIC
## LEBANON 0.2201 [0.2194; 0.2208] 0.1 1.8 UMIC
## LITHUANIA 0.1815 [0.1807; 0.1823] 0.0 1.8 HIC
## LUXEMBOURG 2.6071 [2.5997; 2.6145] 0.1 1.8 HIC
## MEXICO 0.2079 [0.2077; 0.2080] 1.3 1.8 UMIC
## MOROCCO 0.0335 [0.0334; 0.0336] 0.1 1.8 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0279 [0.0276; 0.0281] 0.0 1.8 HIC
## NORWAY 2.1852 [2.1830; 2.1873] 0.6 1.8 HIC
## PAKISTAN 0.0910 [0.0909; 0.0911] 0.9 1.8 LMIC
## PERU 0.0375 [0.0374; 0.0376] 0.1 1.8 UMIC
## PHILIPPINES 0.0372 [0.0371; 0.0372] 0.2 1.8 LMIC
## POLAND 0.0071 [0.0070; 0.0071] 0.0 1.8 HIC
## PORTUGAL 2.2539 [2.2525; 2.2554] 1.3 1.8 HIC
## PUERTO RICO 1.2524 [1.2505; 1.2543] 0.2 1.8 HIC
## ROMANIA 0.4295 [0.4290; 0.4300] 0.5 1.8 UMIC
## RUSSIA 0.0284 [0.0284; 0.0285] 0.2 1.8 UMIC
## SAUDI ARABIA 0.3084 [0.3081; 0.3088] 0.4 1.8 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.1281 [1.1266; 1.1296] 0.3 1.8 HIC
## SLOVENIA 1.3641 [1.3615; 1.3668] 0.1 1.8 HIC
## SOUTH AFRICA 0.1130 [0.1129; 0.1132] 0.3 1.8 UMIC
## SOUTH KOREA 0.4698 [0.4694; 0.4701] 1.2 1.8 HIC
## SPAIN 2.6137 [2.6129; 2.6145] 6.5 1.8 HIC
## SWEDEN 2.7350 [2.7332; 2.7368] 1.4 1.8 HIC
## SWITZERLAND 1.5463 [1.5449; 1.5478] 0.6 1.8 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.1176 [0.1172; 0.1179] 0.1 1.8 LMIC
## TÜRKIYE 0.2955 [0.2953; 0.2957] 1.1 1.8 UMIC
## UNITED ARAB EMIRATES 0.3381 [0.3374; 0.3387] 0.1 1.8 HIC
## UNITED KINGDOM 1.8136 [1.8130; 1.8141] 6.1 1.8 HIC
## UNITED STATES 2.2201 [2.2199; 2.2204] 36.4 1.8 HIC
## URUGUAY 0.1756 [0.1748; 0.1763] 0.0 1.8 HIC
## VENEZUELA 0.2484 [0.2480; 0.2487] 0.4 1.8 UMIC
##
## Number of studies combined: k = 57
##
## rate 95%-CI z p-value
## Common effect model 1.3437 [1.3436; 1.3438] 7718.17 0
## Random effects model 0.3024 [0.2252; 0.4060] -7.96 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2880 [1.1678; 3.4536]; tau = 1.1349 [1.0806; 1.8584]
## I^2 = 100.0%; H = 3620.06
##
## Test of heterogeneity:
## Q d.f. p-value
## 733870312.71 56 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 35 1.9933 [1.9932; 1.9935] 88346534.61 100.0%
## income = UMIC 16 0.1888 [0.1887; 0.1888] 20128030.32 100.0%
## income = LMIC 6 0.1135 [0.1135; 0.1135] 3317777.18 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 622077970.60 2 0
## Within groups 111792342.11 54 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 35 0.6633 [0.5732; 0.7675] 0.1939 0.4404
## income = UMIC 16 0.0943 [0.0635; 0.1401] 0.6523 0.8076
## income = LMIC 6 0.0691 [0.0488; 0.0977] 0.1878 0.4334
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 195.89 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.3584 [0.3581; 0.3587] 0.6 1.7 UMIC
## ARGENTINA 0.2832 [0.2829; 0.2835] 0.5 1.7 UMIC
## AUSTRALIA 0.8531 [0.8524; 0.8537] 0.9 1.7 HIC
## AUSTRIA 1.2971 [1.2959; 1.2984] 0.5 1.7 HIC
## BELARUS 0.0057 [0.0056; 0.0058] 0.0 1.7 UMIC
## BELGIUM 1.7617 [1.7604; 1.7630] 0.9 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0598 [0.0597; 0.0598] 0.5 1.7 UMIC
## BULGARIA 0.1647 [0.1642; 0.1652] 0.1 1.7 UMIC
## CANADA 2.5654 [2.5645; 2.5663] 4.1 1.7 HIC
## CHILE 0.2310 [0.2307; 0.2314] 0.2 1.7 HIC
## CHINA 0.0000 [0.0000; 0.0000] 0.0 1.7 UMIC
## COLOMBIA 0.0525 [0.0524; 0.0526] 0.1 1.7 UMIC
## CROATIA 0.1874 [0.1868; 0.1881] 0.0 1.7 HIC
## CZECH REPUBLIC 0.7723 [0.7714; 0.7731] 0.4 1.7 HIC
## ECUADOR 0.2196 [0.2193; 0.2200] 0.2 1.7 UMIC
## EGYPT 0.1329 [0.1327; 0.1330] 0.5 1.7 LMIC
## ESTONIA 0.1949 [0.1936; 0.1961] 0.0 1.7 HIC
## FINLAND 4.4528 [4.4499; 4.4558] 1.1 1.7 HIC
## FRANCE 2.9223 [2.9216; 2.9230] 8.5 1.7 HIC
## GERMANY 2.1889 [2.1883; 2.1894] 8.2 1.7 HIC
## GREECE 2.0220 [2.0206; 2.0234] 1.0 1.7 HIC
## HUNGARY 0.6597 [0.6589; 0.6605] 0.3 1.7 HIC
## INDIA 0.1545 [0.1545; 0.1546] 8.9 1.7 LMIC
## IRELAND 3.0900 [3.0873; 3.0926] 0.7 1.7 HIC
## ITALY 1.3033 [1.3028; 1.3037] 3.6 1.7 HIC
## JAPAN 0.1340 [0.1339; 0.1341] 0.8 1.7 HIC
## JORDAN 0.0748 [0.0744; 0.0751] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1322 [0.1315; 0.1329] 0.0 1.7 HIC
## LATVIA 0.0932 [0.0925; 0.0939] 0.0 1.7 HIC
## LEBANON 0.2822 [0.2814; 0.2830] 0.1 1.7 UMIC
## LITHUANIA 0.2761 [0.2751; 0.2770] 0.0 1.7 HIC
## LUXEMBOURG 2.8977 [2.8900; 2.9055] 0.1 1.7 HIC
## MEXICO 0.2123 [0.2122; 0.2124] 1.1 1.7 UMIC
## MOROCCO 0.0372 [0.0371; 0.0373] 0.1 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0433 [0.0429; 0.0436] 0.0 1.7 HIC
## NORWAY 2.1474 [2.1452; 2.1495] 0.5 1.7 HIC
## PAKISTAN 0.1127 [0.1127; 0.1128] 0.9 1.7 LMIC
## PERU 0.0528 [0.0527; 0.0530] 0.1 1.7 UMIC
## PHILIPPINES 0.0396 [0.0395; 0.0397] 0.2 1.7 LMIC
## POLAND 0.0090 [0.0089; 0.0090] 0.0 1.7 HIC
## PORTUGAL 2.6513 [2.6496; 2.6529] 1.3 1.7 HIC
## PUERTO RICO 1.2272 [1.2253; 1.2291] 0.2 1.7 HIC
## ROMANIA 0.3411 [0.3407; 0.3415] 0.3 1.7 UMIC
## RUSSIA 0.0725 [0.0724; 0.0725] 0.5 1.7 UMIC
## SAUDI ARABIA 0.3637 [0.3633; 0.3641] 0.5 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.4980 [1.4963; 1.4997] 0.4 1.7 HIC
## SLOVENIA 1.6411 [1.6382; 1.6440] 0.2 1.7 HIC
## SOUTH AFRICA 0.1369 [0.1367; 0.1371] 0.3 1.7 UMIC
## SOUTH KOREA 0.5801 [0.5797; 0.5804] 1.3 1.7 HIC
## SPAIN 3.0765 [3.0757; 3.0773] 6.7 1.7 HIC
## SWEDEN 3.0492 [3.0473; 3.0510] 1.3 1.7 HIC
## SWITZERLAND 1.8991 [1.8975; 1.9007] 0.7 1.7 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0669 [0.0668; 0.0670] 0.2 1.7 UMIC
## TUNISIA 0.1599 [0.1595; 0.1603] 0.1 1.7 LMIC
## TÜRKIYE 0.3880 [0.3878; 0.3883] 1.3 1.7 UMIC
## UNITED ARAB EMIRATES 0.6148 [0.6140; 0.6157] 0.2 1.7 HIC
## UNITED KINGDOM 2.3866 [2.3860; 2.3873] 7.0 1.7 HIC
## UNITED STATES 2.1808 [2.1805; 2.1811] 31.3 1.7 HIC
## URUGUAY 0.3082 [0.3073; 0.3092] 0.0 1.7 HIC
## VENEZUELA 0.2893 [0.2890; 0.2896] 0.4 1.7 UMIC
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 1.3708 [1.3707; 1.3709] 8842.94 0
## Random effects model 0.3166 [0.2367; 0.4235] -7.75 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.3219 [1.1966; 3.3619]; tau = 1.1498 [1.0939; 1.8336]
## I^2 = 100.0%; H = 3905.31
##
## Test of heterogeneity:
## Q d.f. p-value
## 899835081.38 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 36 2.1150 [2.1148; 2.1151] 143362365.55 100.0%
## income = UMIC 18 0.2005 [0.2004; 0.2006] 24514517.79 100.0%
## income = LMIC 6 0.1449 [0.1448; 0.1449] 3888816.70 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 728069381.34 2 0
## Within groups 171765700.04 57 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 36 0.7848 [0.6626; 0.9296] 0.2685 0.5181
## income = UMIC 18 0.0782 [0.0554; 0.1105] 0.5575 0.7466
## income = LMIC 6 0.0904 [0.0657; 0.1243] 0.1588 0.3985
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 228.78 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5023 [0.5019; 0.5026] 0.7 1.6 UMIC
## ARGENTINA 0.4871 [0.4868; 0.4875] 0.8 1.6 UMIC
## AUSTRALIA 1.0422 [1.0415; 1.0429] 0.9 1.6 HIC
## AUSTRIA 1.6069 [1.6055; 1.6083] 0.5 1.6 HIC
## BELARUS 0.0049 [0.0049; 0.0050] 0.0 1.6 UMIC
## BELGIUM 2.1459 [2.1444; 2.1473] 0.9 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0047 [0.0046; 0.0048] 0.0 1.6 UMIC
## BRAZIL 0.0849 [0.0848; 0.0849] 0.7 1.6 UMIC
## BULGARIA 0.2681 [0.2675; 0.2687] 0.1 1.6 UMIC
## CANADA 2.8489 [2.8480; 2.8498] 3.8 1.6 HIC
## CHILE 0.2907 [0.2903; 0.2912] 0.2 1.6 HIC
## CHINA 0.0006 [0.0006; 0.0006] 0.0 1.6 UMIC
## COLOMBIA 0.0601 [0.0600; 0.0602] 0.1 1.6 UMIC
## CROATIA 0.2490 [0.2483; 0.2498] 0.0 1.6 HIC
## CZECH REPUBLIC 1.0325 [1.0315; 1.0335] 0.4 1.6 HIC
## ECUADOR 0.2650 [0.2645; 0.2654] 0.2 1.6 UMIC
## EGYPT 0.2101 [0.2100; 0.2103] 0.7 1.6 LMIC
## ESTONIA 0.2309 [0.2295; 0.2322] 0.0 1.6 HIC
## FINLAND 4.7455 [4.7425; 4.7485] 1.0 1.6 HIC
## FRANCE 3.1118 [3.1111; 3.1125] 7.7 1.6 HIC
## GERMANY 2.3903 [2.3897; 2.3909] 7.5 1.6 HIC
## GREECE 2.1845 [2.1831; 2.1860] 0.9 1.6 HIC
## HUNGARY 0.7808 [0.7799; 0.7817] 0.3 1.6 HIC
## INDIA 0.1927 [0.1926; 0.1927] 9.4 1.6 LMIC
## IRELAND 3.7947 [3.7917; 3.7976] 0.7 1.6 HIC
## ITALY 1.4936 [1.4931; 1.4942] 3.5 1.6 HIC
## JAPAN 0.9483 [0.9481; 0.9486] 4.8 1.6 HIC
## JORDAN 0.0969 [0.0965; 0.0972] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0096 [0.0095; 0.0097] 0.0 1.6 UMIC
## KUWAIT 0.1977 [0.1969; 0.1985] 0.0 1.6 HIC
## LATVIA 0.0698 [0.0692; 0.0703] 0.0 1.6 HIC
## LEBANON 0.4274 [0.4265; 0.4283] 0.1 1.6 UMIC
## LITHUANIA 0.4855 [0.4842; 0.4868] 0.1 1.6 HIC
## LUXEMBOURG 3.0533 [3.0455; 3.0612] 0.1 1.6 HIC
## MEXICO 0.1896 [0.1894; 0.1897] 0.9 1.6 UMIC
## MOROCCO 0.0429 [0.0428; 0.0430] 0.1 1.6 LMIC
## NETHERLANDS 1.8776 [1.8765; 1.8787] 1.2 1.6 HIC
## NEW ZEALAND 0.0559 [0.0555; 0.0562] 0.0 1.6 HIC
## NORWAY 2.4446 [2.4424; 2.4469] 0.5 1.6 HIC
## PAKISTAN 0.1482 [0.1481; 0.1483] 1.1 1.6 LMIC
## PERU 0.0621 [0.0619; 0.0622] 0.1 1.6 UMIC
## PHILIPPINES 0.0455 [0.0455; 0.0456] 0.2 1.6 LMIC
## POLAND 0.0169 [0.0169; 0.0170] 0.0 1.6 HIC
## PORTUGAL 2.7607 [2.7590; 2.7623] 1.1 1.6 HIC
## PUERTO RICO 1.2731 [1.2712; 1.2750] 0.2 1.6 HIC
## ROMANIA 0.3141 [0.3137; 0.3145] 0.2 1.6 UMIC
## RUSSIA 0.1415 [0.1414; 0.1416] 0.8 1.6 UMIC
## SAUDI ARABIA 0.4245 [0.4241; 0.4249] 0.5 1.6 HIC
## SERBIA 0.0287 [0.0285; 0.0289] 0.0 1.6 UMIC
## SLOVAKIA 2.0042 [2.0022; 2.0062] 0.4 1.6 HIC
## SLOVENIA 1.8618 [1.8587; 1.8649] 0.1 1.6 HIC
## SOUTH AFRICA 0.1591 [0.1589; 0.1592] 0.3 1.6 UMIC
## SOUTH KOREA 0.6952 [0.6948; 0.6956] 1.4 1.6 HIC
## SPAIN 3.5436 [3.5427; 3.5445] 6.5 1.6 HIC
## SWEDEN 3.2395 [3.2376; 3.2413] 1.2 1.6 HIC
## SWITZERLAND 2.1983 [2.1966; 2.2000] 0.7 1.6 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.1031 [0.1030; 0.1033] 0.3 1.6 UMIC
## TUNISIA 0.1955 [0.1950; 0.1959] 0.1 1.6 LMIC
## TÜRKIYE 0.5242 [0.5240; 0.5245] 1.5 1.6 UMIC
## UNITED ARAB EMIRATES 0.6845 [0.6836; 0.6854] 0.2 1.6 HIC
## UNITED KINGDOM 2.9403 [2.9396; 2.9409] 7.3 1.6 HIC
## UNITED STATES 2.1866 [2.1863; 2.1868] 26.6 1.6 HIC
## URUGUAY 0.3948 [0.3937; 0.3959] 0.1 1.6 HIC
## VENEZUELA 0.3710 [0.3706; 0.3713] 0.4 1.6 UMIC
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 1.4264 [1.4263; 1.4265] 10868.38 0
## Random effects model 0.3605 [0.2762; 0.4705] -7.51 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1812 [1.1393; 3.0693]; tau = 1.0868 [1.0674; 1.7519]
## I^2 = 100.0%; H = 3958.38
##
## Test of heterogeneity:
## Q d.f. p-value
## 987134380.25 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 2.2143 [2.2142; 2.2145] 141815376.74 100.0%
## income = UMIC 21 0.2544 [0.2543; 0.2544] 43490994.08 100.0%
## income = LMIC 6 0.1837 [0.1837; 0.1838] 4956344.21 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 796871665.22 2 0
## Within groups 190262715.03 61 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.9960 [0.8568; 1.1578] 0.2183 0.4673
## income = UMIC 21 0.0834 [0.0579; 0.1202] 0.7308 0.8549
## income = LMIC 6 0.1148 [0.0845; 0.1560] 0.1468 0.3832
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 260.99 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5436 [0.5432; 0.5440] 0.7 1.5 UMIC
## ARGENTINA 0.6692 [0.6688; 0.6696] 1.0 1.5 UMIC
## AUSTRALIA 1.1969 [1.1962; 1.1977] 0.9 1.5 HIC
## AUSTRIA 1.9266 [1.9250; 1.9281] 0.6 1.5 HIC
## BELARUS 0.0061 [0.0060; 0.0062] 0.0 1.5 UMIC
## BELGIUM 2.2474 [2.2459; 2.2489] 0.9 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0084 [0.0082; 0.0085] 0.0 1.5 UMIC
## BRAZIL 0.1127 [0.1126; 0.1128] 0.8 1.5 UMIC
## BULGARIA 0.0647 [0.0644; 0.0650] 0.0 1.5 UMIC
## CANADA 3.1446 [3.1436; 3.1456] 3.8 1.5 HIC
## CHILE 0.3808 [0.3804; 0.3813] 0.2 1.5 HIC
## CHINA 0.0017 [0.0017; 0.0017] 0.1 1.5 UMIC
## COLOMBIA 0.0711 [0.0710; 0.0712] 0.1 1.5 UMIC
## CROATIA 0.3248 [0.3239; 0.3257] 0.0 1.5 HIC
## CZECH REPUBLIC 1.1044 [1.1034; 1.1055] 0.4 1.5 HIC
## ECUADOR 0.3125 [0.3120; 0.3129] 0.2 1.5 UMIC
## EGYPT 0.3476 [0.3474; 0.3478] 1.0 1.5 LMIC
## ESTONIA 0.2598 [0.2584; 0.2613] 0.0 1.5 HIC
## FINLAND 5.0579 [5.0548; 5.0610] 0.9 1.5 HIC
## FRANCE 3.4227 [3.4219; 3.4234] 7.5 1.5 HIC
## GERMANY 2.6294 [2.6288; 2.6300] 7.3 1.5 HIC
## GREECE 2.1222 [2.1208; 2.1237] 0.8 1.5 HIC
## HUNGARY 0.9178 [0.9168; 0.9188] 0.3 1.5 HIC
## INDIA 0.2189 [0.2188; 0.2189] 9.5 1.5 LMIC
## IRELAND 4.8273 [4.8240; 4.8306] 0.8 1.5 HIC
## ITALY 1.6178 [1.6172; 1.6183] 3.3 1.5 HIC
## JAPAN 1.6550 [1.6546; 1.6554] 7.3 1.5 HIC
## JORDAN 0.1713 [0.1709; 0.1718] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0219 [0.0218; 0.0220] 0.0 1.5 UMIC
## KUWAIT 0.3129 [0.3119; 0.3139] 0.0 1.5 HIC
## LATVIA 0.0739 [0.0733; 0.0745] 0.0 1.5 HIC
## LEBANON 0.4941 [0.4931; 0.4951] 0.1 1.5 UMIC
## LITHUANIA 0.5840 [0.5826; 0.5855] 0.1 1.5 HIC
## LUXEMBOURG 3.3108 [3.3027; 3.3189] 0.1 1.5 HIC
## MEXICO 0.1834 [0.1833; 0.1835] 0.7 1.5 UMIC
## MOROCCO 0.0481 [0.0479; 0.0482] 0.1 1.5 LMIC
## NETHERLANDS 2.1870 [2.1858; 2.1881] 1.3 1.5 HIC
## NEW ZEALAND 0.0606 [0.0602; 0.0610] 0.0 1.5 HIC
## NORWAY 2.6430 [2.6406; 2.6454] 0.5 1.5 HIC
## PAKISTAN 0.1655 [0.1654; 0.1656] 1.1 1.5 LMIC
## PERU 0.0764 [0.0762; 0.0766] 0.1 1.5 UMIC
## PHILIPPINES 0.0513 [0.0512; 0.0513] 0.2 1.5 LMIC
## POLAND 0.0124 [0.0124; 0.0125] 0.0 1.5 HIC
## PORTUGAL 3.1609 [3.1591; 3.1627] 1.1 1.5 HIC
## PUERTO RICO 1.2744 [1.2724; 1.2763] 0.2 1.5 HIC
## ROMANIA 0.2308 [0.2305; 0.2312] 0.2 1.5 UMIC
## RUSSIA 0.2983 [0.2982; 0.2985] 1.5 1.5 UMIC
## SAUDI ARABIA 0.6099 [0.6094; 0.6103] 0.6 1.5 HIC
## SERBIA 0.0514 [0.0512; 0.0517] 0.0 1.5 UMIC
## SLOVAKIA 2.1634 [2.1614; 2.1655] 0.4 1.5 HIC
## SLOVENIA 2.0907 [2.0875; 2.0940] 0.1 1.5 HIC
## SOUTH AFRICA 0.1847 [0.1845; 0.1849] 0.3 1.5 UMIC
## SOUTH KOREA 1.0076 [1.0072; 1.0081] 1.7 1.5 HIC
## SPAIN 3.9095 [3.9085; 3.9104] 6.3 1.5 HIC
## SWEDEN 3.2022 [3.2003; 3.2040] 1.0 1.5 HIC
## SWITZERLAND 2.4428 [2.4410; 2.4445] 0.7 1.5 HIC
## TAIWAN 0.0082 [0.0082; 0.0083] 0.0 1.5 HIC
## THAILAND 0.1173 [0.1172; 0.1175] 0.3 1.5 UMIC
## TUNISIA 0.2953 [0.2948; 0.2959] 0.1 1.5 LMIC
## TÜRKIYE 0.7695 [0.7692; 0.7699] 2.0 1.5 UMIC
## UNITED ARAB EMIRATES 0.2766 [0.2760; 0.2772] 0.1 1.5 HIC
## UNITED KINGDOM 3.4816 [3.4808; 3.4823] 7.7 1.5 HIC
## UNITED STATES 2.0969 [2.0967; 2.0972] 22.6 1.5 HIC
## URUGUAY 0.6758 [0.6743; 0.6772] 0.1 1.5 HIC
## VENEZUELA 0.4633 [0.4629; 0.4637] 0.5 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.5211 [1.5210; 1.5212] 13684.51 0
## Random effects model 0.4013 [0.3103; 0.5191] -6.95 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1201 [1.1273; 3.0014]; tau = 1.0583 [1.0617; 1.7325]
## I^2 = 100.0%; H = 4114.94
##
## Test of heterogeneity:
## Q d.f. p-value
## 1083692664.03 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 2.3945 [2.3944; 2.3947] 139373093.33 100.0%
## income = UMIC 21 0.3334 [0.3333; 0.3334] 71090434.70 100.0%
## income = LMIC 6 0.2167 [0.2167; 0.2167] 8499546.53 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 864729589.47 2 0
## Within groups 218963074.56 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.9818 [0.8552; 1.1272] 0.1886 0.4343
## income = UMIC 21 0.1064 [0.0709; 0.1598] 0.9027 0.9501
## income = LMIC 6 0.1447 [0.1020; 0.2052] 0.1911 0.4372
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 181.16 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.6123 [0.6119; 0.6128] 0.7 1.5 UMIC
## ARGENTINA 0.8307 [0.8302; 0.8311] 1.0 1.5 UMIC
## AUSTRALIA 3.1224 [3.1212; 3.1236] 2.1 1.5 HIC
## AUSTRIA 2.1375 [2.1358; 2.1391] 0.5 1.5 HIC
## BELARUS 0.0123 [0.0122; 0.0124] 0.0 1.5 UMIC
## BELGIUM 2.0611 [2.0597; 2.0625] 0.7 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0126 [0.0124; 0.0128] 0.0 1.5 UMIC
## BRAZIL 0.1486 [0.1485; 0.1487] 0.9 1.5 UMIC
## BULGARIA 0.0809 [0.0806; 0.0813] 0.0 1.5 UMIC
## CANADA 3.7305 [3.7294; 3.7315] 3.9 1.5 HIC
## CHILE 0.4682 [0.4676; 0.4687] 0.2 1.5 HIC
## CHINA 0.0024 [0.0024; 0.0024] 0.1 1.5 UMIC
## COLOMBIA 0.0892 [0.0891; 0.0893] 0.1 1.5 UMIC
## CROATIA 0.3720 [0.3710; 0.3730] 0.0 1.5 HIC
## CZECH REPUBLIC 1.3551 [1.3539; 1.3562] 0.4 1.5 HIC
## ECUADOR 0.3316 [0.3311; 0.3320] 0.2 1.5 UMIC
## EGYPT 0.4690 [0.4688; 0.4693] 1.2 1.5 LMIC
## ESTONIA 0.3397 [0.3381; 0.3414] 0.0 1.5 HIC
## FINLAND 5.3754 [5.3721; 5.3786] 0.9 1.5 HIC
## FRANCE 3.6856 [3.6849; 3.6864] 6.9 1.5 HIC
## GERMANY 2.8213 [2.8207; 2.8219] 6.7 1.5 HIC
## GREECE 2.3245 [2.3230; 2.3260] 0.7 1.5 HIC
## HUNGARY 1.0685 [1.0674; 1.0696] 0.3 1.5 HIC
## INDIA 0.2404 [0.2404; 0.2405] 9.0 1.5 LMIC
## IRELAND 6.0138 [6.0100; 6.0175] 0.8 1.5 HIC
## ITALY 1.7605 [1.7600; 1.7611] 3.1 1.5 HIC
## JAPAN 2.1922 [2.1917; 2.1926] 8.3 1.5 HIC
## JORDAN 0.3112 [0.3106; 0.3118] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0286 [0.0284; 0.0287] 0.0 1.5 UMIC
## KUWAIT 0.6898 [0.6884; 0.6913] 0.1 1.5 HIC
## LATVIA 0.0938 [0.0931; 0.0945] 0.0 1.5 HIC
## LEBANON 0.5122 [0.5112; 0.5131] 0.1 1.5 UMIC
## LITHUANIA 0.7074 [0.7058; 0.7089] 0.1 1.5 HIC
## LUXEMBOURG 3.3751 [3.3670; 3.3832] 0.1 1.5 HIC
## MEXICO 0.1822 [0.1821; 0.1823] 0.6 1.5 UMIC
## MOROCCO 0.0512 [0.0510; 0.0513] 0.1 1.5 LMIC
## NETHERLANDS 2.4227 [2.4214; 2.4239] 1.2 1.5 HIC
## NEW ZEALAND 0.0756 [0.0752; 0.0760] 0.0 1.5 HIC
## NORWAY 2.7219 [2.7196; 2.7243] 0.4 1.5 HIC
## PAKISTAN 0.1842 [0.1841; 0.1843] 1.0 1.5 LMIC
## PERU 0.0920 [0.0918; 0.0922] 0.1 1.5 UMIC
## PHILIPPINES 0.0583 [0.0583; 0.0584] 0.2 1.5 LMIC
## POLAND 0.0159 [0.0159; 0.0160] 0.0 1.5 HIC
## PORTUGAL 3.1361 [3.1343; 3.1379] 1.0 1.5 HIC
## PUERTO RICO 1.2648 [1.2629; 1.2668] 0.1 1.5 HIC
## ROMANIA 0.1968 [0.1965; 0.1972] 0.1 1.5 UMIC
## RUSSIA 0.6995 [0.6993; 0.6997] 3.0 1.5 UMIC
## SAUDI ARABIA 0.9389 [0.9383; 0.9395] 0.8 1.5 HIC
## SERBIA 0.1583 [0.1578; 0.1587] 0.0 1.5 UMIC
## SLOVAKIA 2.2623 [2.2602; 2.2644] 0.4 1.5 HIC
## SLOVENIA 2.2580 [2.2546; 2.2614] 0.1 1.5 HIC
## SOUTH AFRICA 0.1957 [0.1955; 0.1959] 0.3 1.5 UMIC
## SOUTH KOREA 1.1367 [1.1362; 1.1372] 1.7 1.5 HIC
## SPAIN 4.2439 [4.2429; 4.2448] 5.8 1.5 HIC
## SWEDEN 3.2839 [3.2820; 3.2858] 0.9 1.5 HIC
## SWITZERLAND 2.6708 [2.6689; 2.6726] 0.6 1.5 HIC
## TAIWAN 0.0939 [0.0937; 0.0941] 0.1 1.5 HIC
## THAILAND 0.1231 [0.1230; 0.1233] 0.2 1.5 UMIC
## TUNISIA 0.3557 [0.3552; 0.3563] 0.1 1.5 LMIC
## TÜRKIYE 1.0282 [1.0279; 1.0286] 2.3 1.5 UMIC
## UNITED ARAB EMIRATES 0.4835 [0.4828; 0.4843] 0.1 1.5 HIC
## UNITED KINGDOM 4.2010 [4.2002; 4.2019] 8.0 1.5 HIC
## UNITED STATES 2.2311 [2.2308; 2.2313] 20.7 1.5 HIC
## URUGUAY 0.7672 [0.7657; 0.7688] 0.1 1.5 HIC
## VENEZUELA 0.6107 [0.6102; 0.6111] 0.5 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.7083 [1.7082; 1.7084] 18889.06 0
## Random effects model 0.5137 [0.3997; 0.6602] -5.20 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0655 [1.0709; 2.8146]; tau = 1.0323 [1.0349; 1.6777]
## I^2 = 100.0%; H = 4358.34
##
## Test of heterogeneity:
## Q d.f. p-value
## 1215688307.80 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 2.6903 [2.6902; 2.6905] 155938952.31 100.0%
## income = UMIC 21 0.4937 [0.4936; 0.4938] 103646460.06 100.0%
## income = LMIC 6 0.2459 [0.2459; 0.2460] 13552796.86 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 942550098.57 2 0
## Within groups 273138209.23 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.2461 [1.0878; 1.4274] 0.1826 0.4273
## income = UMIC 21 0.1424 [0.0937; 0.2163] 0.9552 0.9773
## income = LMIC 6 0.1675 [0.1123; 0.2497] 0.2491 0.4991
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 164.21 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.8746 [0.8741; 0.8750] 0.9 1.5 UMIC
## ARGENTINA 0.9517 [0.9512; 0.9522] 1.0 1.5 UMIC
## AUSTRALIA 5.4618 [5.4602; 5.4633] 3.3 1.5 HIC
## AUSTRIA 2.3277 [2.3261; 2.3294] 0.5 1.5 HIC
## BELARUS 0.0146 [0.0145; 0.0147] 0.0 1.5 UMIC
## BELGIUM 2.0963 [2.0949; 2.0978] 0.6 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0180 [0.0177; 0.0182] 0.0 1.5 UMIC
## BRAZIL 0.1909 [0.1908; 0.1910] 1.0 1.5 UMIC
## BULGARIA 0.1039 [0.1035; 0.1043] 0.0 1.5 UMIC
## CANADA 4.2330 [4.2318; 4.2341] 3.8 1.5 HIC
## CHILE 0.5657 [0.5651; 0.5663] 0.3 1.5 HIC
## CHINA 0.0026 [0.0026; 0.0026] 0.1 1.5 UMIC
## COLOMBIA 0.1081 [0.1080; 0.1083] 0.1 1.5 UMIC
## CROATIA 0.4368 [0.4358; 0.4379] 0.0 1.5 HIC
## CZECH REPUBLIC 1.5417 [1.5405; 1.5430] 0.4 1.5 HIC
## ECUADOR 0.3778 [0.3774; 0.3783] 0.2 1.5 UMIC
## EGYPT 0.6020 [0.6018; 0.6023] 1.4 1.5 LMIC
## ESTONIA 0.4430 [0.4412; 0.4449] 0.0 1.5 HIC
## FINLAND 4.9036 [4.9005; 4.9067] 0.7 1.5 HIC
## FRANCE 4.0148 [4.0140; 4.0156] 6.6 1.5 HIC
## GERMANY 3.0256 [3.0250; 3.0263] 6.3 1.5 HIC
## GREECE 2.3194 [2.3179; 2.3209] 0.6 1.5 HIC
## HUNGARY 1.1791 [1.1780; 1.1803] 0.3 1.5 HIC
## INDIA 0.2673 [0.2672; 0.2673] 8.8 1.5 LMIC
## IRELAND 6.5956 [6.5917; 6.5995] 0.8 1.5 HIC
## ITALY 1.9060 [1.9054; 1.9065] 2.9 1.5 HIC
## JAPAN 2.7970 [2.7965; 2.7974] 9.1 1.5 HIC
## JORDAN 0.4901 [0.4893; 0.4909] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0399 [0.0398; 0.0401] 0.0 1.5 UMIC
## KUWAIT 0.5753 [0.5740; 0.5766] 0.1 1.5 HIC
## LATVIA 0.1005 [0.0998; 0.1013] 0.0 1.5 HIC
## LEBANON 0.5807 [0.5797; 0.5817] 0.1 1.5 UMIC
## LITHUANIA 0.8691 [0.8674; 0.8709] 0.1 1.5 HIC
## LUXEMBOURG 3.5380 [3.5298; 3.5462] 0.0 1.5 HIC
## MEXICO 0.2060 [0.2059; 0.2062] 0.6 1.5 UMIC
## MOROCCO 0.0548 [0.0547; 0.0550] 0.0 1.5 LMIC
## NETHERLANDS 2.6728 [2.6715; 2.6741] 1.2 1.5 HIC
## NEW ZEALAND 0.0869 [0.0864; 0.0873] 0.0 1.5 HIC
## NORWAY 2.8620 [2.8596; 2.8645] 0.4 1.5 HIC
## PAKISTAN 0.2062 [0.2061; 0.2063] 1.0 1.5 LMIC
## PERU 0.1047 [0.1045; 0.1049] 0.1 1.5 UMIC
## PHILIPPINES 0.0678 [0.0678; 0.0679] 0.2 1.5 LMIC
## POLAND 0.0388 [0.0387; 0.0389] 0.0 1.5 HIC
## PORTUGAL 3.6144 [3.6125; 3.6163] 1.0 1.5 HIC
## PUERTO RICO 1.2472 [1.2452; 1.2491] 0.1 1.5 HIC
## ROMANIA 0.1747 [0.1744; 0.1750] 0.1 1.5 UMIC
## RUSSIA 0.9011 [0.9009; 0.9014] 3.3 1.5 UMIC
## SAUDI ARABIA 1.2680 [1.2673; 1.2687] 1.0 1.5 HIC
## SERBIA 0.3061 [0.3055; 0.3067] 0.1 1.5 UMIC
## SLOVAKIA 2.3157 [2.3136; 2.3179] 0.3 1.5 HIC
## SLOVENIA 2.6181 [2.6145; 2.6218] 0.1 1.5 HIC
## SOUTH AFRICA 0.2145 [0.2143; 0.2147] 0.3 1.5 UMIC
## SOUTH KOREA 1.2171 [1.2166; 1.2176] 1.6 1.5 HIC
## SPAIN 4.4807 [4.4797; 4.4817] 5.3 1.5 HIC
## SWEDEN 3.2563 [3.2544; 3.2582] 0.8 1.5 HIC
## SWITZERLAND 2.9188 [2.9169; 2.9207] 0.6 1.5 HIC
## TAIWAN 0.1689 [0.1687; 0.1692] 0.1 1.5 HIC
## THAILAND 0.1475 [0.1474; 0.1477] 0.3 1.5 UMIC
## TUNISIA 0.3975 [0.3969; 0.3982] 0.1 1.5 LMIC
## TÜRKIYE 1.5192 [1.5187; 1.5196] 3.0 1.5 UMIC
## UNITED ARAB EMIRATES 0.7207 [0.7198; 0.7216] 0.2 1.5 HIC
## UNITED KINGDOM 5.0509 [5.0500; 5.0518] 8.4 1.5 HIC
## UNITED STATES 2.3340 [2.3338; 2.3343] 19.0 1.5 HIC
## URUGUAY 0.9316 [0.9299; 0.9333] 0.1 1.5 HIC
## VENEZUELA 0.7561 [0.7556; 0.7566] 0.6 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.9313 [1.9312; 1.9314] 24919.17 0
## Random effects model 0.6103 [0.4755; 0.7832] -3.88 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0531 [1.0412; 2.7206]; tau = 1.0262 [1.0204; 1.6494]
## I^2 = 100.0%; H = 4665.04
##
## Test of heterogeneity:
## Q d.f. p-value
## 1392808286.69 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 3.0394 [3.0392; 3.0395] 200279695.07 100.0%
## income = UMIC 21 0.6743 [0.6742; 0.6744] 143118663.44 100.0%
## income = LMIC 6 0.2813 [0.2812; 0.2813] 20324059.78 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1029085868.40 2 0
## Within groups 363722418.29 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.4454 [1.2512; 1.6697] 0.2058 0.4537
## income = UMIC 21 0.1786 [0.1160; 0.2749] 1.0172 1.0085
## income = LMIC 6 0.1913 [0.1226; 0.2986] 0.3096 0.5564
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 139.33 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.0163 [1.0158; 1.0168] 0.9 1.5 UMIC
## ARGENTINA 1.1057 [1.1052; 1.1062] 1.1 1.5 UMIC
## AUSTRALIA 6.8676 [6.8659; 6.8693] 3.8 1.5 HIC
## AUSTRIA 2.4571 [2.4554; 2.4588] 0.5 1.5 HIC
## BELARUS 0.0133 [0.0132; 0.0134] 0.0 1.5 UMIC
## BELGIUM 2.3686 [2.3671; 2.3700] 0.6 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0204 [0.0202; 0.0207] 0.0 1.5 UMIC
## BRAZIL 0.2552 [0.2551; 0.2553] 1.2 1.5 UMIC
## BULGARIA 0.1449 [0.1444; 0.1453] 0.0 1.5 UMIC
## CANADA 4.8298 [4.8286; 4.8310] 4.0 1.5 HIC
## CHILE 0.6778 [0.6772; 0.6785] 0.3 1.5 HIC
## CHINA 0.0034 [0.0034; 0.0034] 0.1 1.5 UMIC
## COLOMBIA 0.1500 [0.1498; 0.1502] 0.2 1.5 UMIC
## CROATIA 0.5667 [0.5655; 0.5679] 0.1 1.5 HIC
## CZECH REPUBLIC 2.0048 [2.0034; 2.0062] 0.5 1.5 HIC
## ECUADOR 0.4372 [0.4367; 0.4377] 0.2 1.5 UMIC
## EGYPT 0.8155 [0.8152; 0.8158] 1.7 1.5 LMIC
## ESTONIA 0.6044 [0.6022; 0.6066] 0.0 1.5 HIC
## FINLAND 4.7511 [4.7480; 4.7541] 0.6 1.5 HIC
## FRANCE 4.2238 [4.2230; 4.2247] 6.3 1.5 HIC
## GERMANY 3.2835 [3.2828; 3.2841] 6.2 1.5 HIC
## GREECE 2.3751 [2.3736; 2.3766] 0.6 1.5 HIC
## HUNGARY 1.1061 [1.1050; 1.1072] 0.2 1.5 HIC
## INDIA 0.3002 [0.3002; 0.3003] 9.1 1.5 LMIC
## IRELAND 8.2475 [8.2432; 8.2519] 0.9 1.5 HIC
## ITALY 1.9980 [1.9974; 1.9986] 2.8 1.5 HIC
## JAPAN 3.0553 [3.0548; 3.0558] 9.0 1.5 HIC
## JORDAN 0.5068 [0.5060; 0.5075] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0641 [0.0639; 0.0643] 0.0 1.5 UMIC
## KUWAIT 0.6329 [0.6316; 0.6342] 0.1 1.5 HIC
## LATVIA 0.0874 [0.0867; 0.0881] 0.0 1.5 HIC
## LEBANON 0.6932 [0.6922; 0.6943] 0.1 1.5 UMIC
## LITHUANIA 0.9057 [0.9039; 0.9075] 0.1 1.5 HIC
## LUXEMBOURG 3.4906 [3.4825; 3.4986] 0.0 1.5 HIC
## MEXICO 0.2250 [0.2249; 0.2252] 0.6 1.5 UMIC
## MOROCCO 0.0612 [0.0611; 0.0613] 0.0 1.5 LMIC
## NETHERLANDS 2.8345 [2.8332; 2.8359] 1.1 1.5 HIC
## NEW ZEALAND 0.1001 [0.0997; 0.1006] 0.0 1.5 HIC
## NORWAY 3.0177 [3.0153; 3.0202] 0.4 1.5 HIC
## PAKISTAN 0.2419 [0.2418; 0.2420] 1.1 1.5 LMIC
## PERU 0.1435 [0.1433; 0.1437] 0.1 1.5 UMIC
## PHILIPPINES 0.0781 [0.0780; 0.0782] 0.2 1.5 LMIC
## POLAND 0.0669 [0.0667; 0.0670] 0.1 1.5 HIC
## PORTUGAL 3.7551 [3.7532; 3.7571] 0.9 1.5 HIC
## PUERTO RICO 1.1861 [1.1842; 1.1881] 0.1 1.5 HIC
## ROMANIA 0.1684 [0.1681; 0.1687] 0.1 1.5 UMIC
## RUSSIA 0.7960 [0.7958; 0.7963] 2.7 1.5 UMIC
## SAUDI ARABIA 1.0275 [1.0269; 1.0281] 0.8 1.5 HIC
## SERBIA 0.6683 [0.6674; 0.6692] 0.1 1.5 UMIC
## SLOVAKIA 2.5507 [2.5485; 2.5529] 0.3 1.5 HIC
## SLOVENIA 2.9286 [2.9248; 2.9325] 0.1 1.5 HIC
## SOUTH AFRICA 0.2358 [0.2356; 0.2360] 0.3 1.5 UMIC
## SOUTH KOREA 1.3430 [1.3425; 1.3435] 1.6 1.5 HIC
## SPAIN 4.4954 [4.4944; 4.4964] 4.8 1.5 HIC
## SWEDEN 3.2796 [3.2777; 3.2815] 0.7 1.5 HIC
## SWITZERLAND 3.2163 [3.2143; 3.2183] 0.6 1.5 HIC
## TAIWAN 0.2086 [0.2083; 0.2089] 0.1 1.5 HIC
## THAILAND 0.1601 [0.1599; 0.1602] 0.3 1.5 UMIC
## TUNISIA 0.4702 [0.4696; 0.4709] 0.1 1.5 LMIC
## TÜRKIYE 2.0758 [2.0753; 2.0764] 3.8 1.5 UMIC
## UNITED ARAB EMIRATES 0.9940 [0.9929; 0.9951] 0.2 1.5 HIC
## UNITED KINGDOM 5.8870 [5.8860; 5.8879] 8.9 1.5 HIC
## UNITED STATES 2.4324 [2.4321; 2.4326] 18.0 1.5 HIC
## URUGUAY 1.1259 [1.1241; 1.1278] 0.1 1.5 HIC
## VENEZUELA 0.7361 [0.7356; 0.7366] 0.5 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.0947 [2.0946; 2.0948] 29431.77 0
## Random effects model 0.6982 [0.5421; 0.8993] -2.78 0.0054
##
## Quantifying heterogeneity:
## tau^2 = 1.0833 [1.0286; 2.6697]; tau = 1.0408 [1.0142; 1.6339]
## I^2 = 100.0%; H = 4981.73
##
## Test of heterogeneity:
## Q d.f. p-value
## 1588329792.44 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 3.3265 [3.3264; 3.3267] 258015807.24 100.0%
## income = UMIC 21 0.7788 [0.7787; 0.7789] 188783525.44 100.0%
## income = LMIC 6 0.3318 [0.3317; 0.3318] 34002240.13 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1107528219.63 2 0
## Within groups 480801572.82 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.6000 [1.3678; 1.8716] 0.2432 0.4932
## income = UMIC 21 0.2150 [0.1357; 0.3406] 1.1575 1.0759
## income = LMIC 6 0.2260 [0.1353; 0.3774] 0.4111 0.6412
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 106.67 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.3989 [1.3983; 1.3995] 1.2 1.5 UMIC
## ARGENTINA 1.1449 [1.1443; 1.1454] 1.1 1.5 UMIC
## AUSTRALIA 8.0026 [8.0007; 8.0044] 4.1 1.5 HIC
## AUSTRIA 3.4469 [3.4449; 3.4489] 0.6 1.5 HIC
## BELARUS 0.0210 [0.0209; 0.0212] 0.0 1.5 UMIC
## BELGIUM 3.6912 [3.6894; 3.6931] 0.9 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0389 [0.0386; 0.0393] 0.0 1.5 UMIC
## BRAZIL 0.3278 [0.3277; 0.3280] 1.4 1.5 UMIC
## BULGARIA 0.4235 [0.4227; 0.4243] 0.1 1.5 UMIC
## CANADA 5.4479 [5.4466; 5.4491] 4.2 1.5 HIC
## CHILE 0.7712 [0.7705; 0.7719] 0.3 1.5 HIC
## CHINA 0.0048 [0.0048; 0.0048] 0.1 1.5 UMIC
## COLOMBIA 0.1520 [0.1518; 0.1521] 0.2 1.5 UMIC
## CROATIA 0.9388 [0.9372; 0.9403] 0.1 1.5 HIC
## CZECH REPUBLIC 2.9635 [2.9618; 2.9652] 0.7 1.5 HIC
## ECUADOR 0.4496 [0.4491; 0.4501] 0.2 1.5 UMIC
## EGYPT 1.1842 [1.1839; 1.1846] 2.4 1.5 LMIC
## ESTONIA 0.8538 [0.8512; 0.8564] 0.0 1.5 HIC
## FINLAND 4.9233 [4.9203; 4.9264] 0.6 1.5 HIC
## FRANCE 4.3282 [4.3274; 4.3290] 5.9 1.5 HIC
## GERMANY 3.5834 [3.5827; 3.5840] 6.2 1.5 HIC
## GREECE 2.9066 [2.9049; 2.9083] 0.7 1.5 HIC
## HUNGARY 1.1861 [1.1849; 1.1872] 0.2 1.5 HIC
## INDIA 0.3294 [0.3293; 0.3294] 9.2 1.5 LMIC
## IRELAND 8.8947 [8.8902; 8.8992] 0.9 1.5 HIC
## ITALY 2.0556 [2.0550; 2.0562] 2.6 1.5 HIC
## JAPAN 3.3683 [3.3678; 3.3688] 9.1 1.5 HIC
## JORDAN 0.8144 [0.8134; 0.8153] 0.2 1.5 UMIC
## KAZAKHSTAN 0.0473 [0.0471; 0.0474] 0.0 1.5 UMIC
## KUWAIT 0.8609 [0.8594; 0.8624] 0.1 1.5 HIC
## LATVIA 0.1269 [0.1260; 0.1277] 0.0 1.5 HIC
## LEBANON 0.8859 [0.8847; 0.8870] 0.1 1.5 UMIC
## LITHUANIA 1.2196 [1.2174; 1.2217] 0.1 1.5 HIC
## LUXEMBOURG 3.6592 [3.6511; 3.6674] 0.0 1.5 HIC
## MEXICO 0.2381 [0.2379; 0.2382] 0.6 1.5 UMIC
## MOROCCO 0.0617 [0.0615; 0.0618] 0.0 1.5 LMIC
## NETHERLANDS 3.0032 [3.0018; 3.0045] 1.1 1.5 HIC
## NEW ZEALAND 0.1140 [0.1135; 0.1145] 0.0 1.5 HIC
## NORWAY 3.1358 [3.1333; 3.1384] 0.3 1.5 HIC
## PAKISTAN 0.2771 [0.2770; 0.2773] 1.2 1.5 LMIC
## PERU 0.1565 [0.1563; 0.1567] 0.1 1.5 UMIC
## PHILIPPINES 0.0931 [0.0930; 0.0932] 0.2 1.5 LMIC
## POLAND 0.3348 [0.3345; 0.3352] 0.3 1.5 HIC
## PORTUGAL 3.5745 [3.5725; 3.5764] 0.8 1.5 HIC
## PUERTO RICO 1.1569 [1.1550; 1.1589] 0.1 1.5 HIC
## ROMANIA 0.1887 [0.1883; 0.1890] 0.1 1.5 UMIC
## RUSSIA 0.1847 [0.1846; 0.1849] 0.6 1.5 UMIC
## SAUDI ARABIA 0.8962 [0.8957; 0.8968] 0.6 1.5 HIC
## SERBIA 1.0159 [1.0148; 1.0170] 0.2 1.5 UMIC
## SLOVAKIA 3.5712 [3.5686; 3.5739] 0.4 1.5 HIC
## SLOVENIA 3.3094 [3.3053; 3.3135] 0.1 1.5 HIC
## SOUTH AFRICA 0.2596 [0.2594; 0.2599] 0.3 1.5 UMIC
## SOUTH KOREA 1.5523 [1.5517; 1.5529] 1.7 1.5 HIC
## SPAIN 4.7605 [4.7595; 4.7615] 4.7 1.5 HIC
## SWEDEN 3.2710 [3.2692; 3.2729] 0.7 1.5 HIC
## SWITZERLAND 3.4599 [3.4578; 3.4619] 0.6 1.5 HIC
## TAIWAN 0.2559 [0.2555; 0.2562] 0.1 1.5 HIC
## THAILAND 0.1710 [0.1709; 0.1712] 0.2 1.5 UMIC
## TUNISIA 0.5303 [0.5296; 0.5310] 0.1 1.5 LMIC
## TÜRKIYE 2.5282 [2.5276; 2.5288] 4.3 1.5 UMIC
## UNITED ARAB EMIRATES 0.7037 [0.7028; 0.7046] 0.1 1.5 HIC
## UNITED KINGDOM 6.7927 [6.7917; 6.7938] 9.5 1.5 HIC
## UNITED STATES 2.4771 [2.4768; 2.4773] 16.9 1.5 HIC
## URUGUAY 1.1942 [1.1923; 1.1961] 0.1 1.5 HIC
## VENEZUELA 0.5555 [0.5550; 0.5559] 0.4 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.3081 [2.3080; 2.3082] 34751.09 0
## Random effects model 0.8178 [0.6314; 1.0592] -1.52 0.1275
##
## Quantifying heterogeneity:
## tau^2 = 1.1323 [1.0552; 2.7138]; tau = 1.0641 [1.0272; 1.6473]
## I^2 = 100.0%; H = 5322.52
##
## Test of heterogeneity:
## Q d.f. p-value
## 1813068895.36 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 3.6291 [3.6289; 3.6293] 325732543.59 100.0%
## income = UMIC 21 0.8608 [0.8606; 0.8609] 264786642.47 100.0%
## income = LMIC 6 0.3997 [0.3996; 0.3997] 67346173.20 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1155203536.11 2 0
## Within groups 657865359.25 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.8988 [1.6051; 2.2464] 0.2794 0.5286
## income = UMIC 21 0.2463 [0.1415; 0.4289] 1.6804 1.2963
## income = LMIC 6 0.2628 [0.1390; 0.4968] 0.6334 0.7958
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 76.78 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 2.9210 [2.9202; 2.9219] 2.3 1.5 UMIC
## ARGENTINA 1.2095 [1.2090; 1.2101] 1.0 1.5 UMIC
## AUSTRALIA 8.6417 [8.6397; 8.6436] 4.0 1.5 HIC
## AUSTRIA 3.6534 [3.6513; 3.6555] 0.6 1.5 HIC
## BELARUS 0.0391 [0.0389; 0.0393] 0.0 1.5 UMIC
## BELGIUM 4.3393 [4.3373; 4.3413] 0.9 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0747 [0.0742; 0.0752] 0.0 1.5 UMIC
## BRAZIL 0.4008 [0.4007; 0.4010] 1.6 1.5 UMIC
## BULGARIA 0.6955 [0.6945; 0.6965] 0.1 1.5 UMIC
## CANADA 6.0241 [6.0228; 6.0254] 4.2 1.5 HIC
## CHILE 0.8596 [0.8589; 0.8603] 0.3 1.5 HIC
## CHINA 0.0065 [0.0065; 0.0066] 0.2 1.5 UMIC
## COLOMBIA 0.1610 [0.1608; 0.1612] 0.1 1.5 UMIC
## CROATIA 1.1064 [1.1047; 1.1081] 0.1 1.5 HIC
## CZECH REPUBLIC 3.5544 [3.5525; 3.5563] 0.7 1.5 HIC
## ECUADOR 0.4751 [0.4746; 0.4757] 0.2 1.5 UMIC
## EGYPT 1.9074 [1.9069; 1.9078] 3.5 1.5 LMIC
## ESTONIA 1.1673 [1.1643; 1.1704] 0.0 1.5 HIC
## FINLAND 5.0831 [5.0800; 5.0862] 0.5 1.5 HIC
## FRANCE 4.4210 [4.4201; 4.4218] 5.4 1.5 HIC
## GERMANY 3.7208 [3.7201; 3.7215] 5.8 1.5 HIC
## GREECE 3.2721 [3.2703; 3.2740] 0.7 1.5 HIC
## HUNGARY 1.2721 [1.2710; 1.2733] 0.2 1.5 HIC
## INDIA 0.3586 [0.3586; 0.3587] 9.1 1.5 LMIC
## IRELAND 8.9694 [8.9649; 8.9739] 0.8 1.5 HIC
## ITALY 2.2635 [2.2629; 2.2641] 2.6 1.5 HIC
## JAPAN 3.7304 [3.7298; 3.7310] 9.0 1.5 HIC
## JORDAN 1.0108 [1.0097; 1.0118] 0.2 1.5 UMIC
## KAZAKHSTAN 0.0662 [0.0660; 0.0664] 0.0 1.5 UMIC
## KUWAIT 1.7104 [1.7083; 1.7125] 0.1 1.5 HIC
## LATVIA 0.1982 [0.1971; 0.1992] 0.0 1.5 HIC
## LEBANON 0.9871 [0.9858; 0.9883] 0.1 1.5 UMIC
## LITHUANIA 1.1829 [1.1808; 1.1850] 0.1 1.5 HIC
## LUXEMBOURG 3.8108 [3.8026; 3.8190] 0.0 1.5 HIC
## MEXICO 0.2819 [0.2817; 0.2820] 0.7 1.5 UMIC
## MOROCCO 0.1094 [0.1092; 0.1095] 0.1 1.5 LMIC
## NETHERLANDS 3.0973 [3.0959; 3.0987] 1.0 1.5 HIC
## NEW ZEALAND 0.1255 [0.1250; 0.1260] 0.0 1.5 HIC
## NORWAY 3.3743 [3.3717; 3.3769] 0.3 1.5 HIC
## PAKISTAN 0.3093 [0.3092; 0.3094] 1.2 1.5 LMIC
## PERU 0.1504 [0.1501; 0.1506] 0.1 1.5 UMIC
## PHILIPPINES 0.1064 [0.1063; 0.1065] 0.2 1.5 LMIC
## POLAND 0.7340 [0.7336; 0.7345] 0.5 1.5 HIC
## PORTUGAL 3.5756 [3.5737; 3.5776] 0.7 1.5 HIC
## PUERTO RICO 1.1721 [1.1701; 1.1741] 0.1 1.5 HIC
## ROMANIA 0.2316 [0.2312; 0.2319] 0.1 1.5 UMIC
## RUSSIA 0.2277 [0.2276; 0.2278] 0.6 1.5 UMIC
## SAUDI ARABIA 1.3068 [1.3061; 1.3074] 0.8 1.5 HIC
## SERBIA 1.3730 [1.3717; 1.3743] 0.2 1.5 UMIC
## SLOVAKIA 3.1730 [3.1705; 3.1755] 0.3 1.5 HIC
## SLOVENIA 3.4915 [3.4873; 3.4957] 0.1 1.5 HIC
## SOUTH AFRICA 0.2695 [0.2693; 0.2697] 0.3 1.5 UMIC
## SOUTH KOREA 1.8268 [1.8262; 1.8274] 1.8 1.5 HIC
## SPAIN 4.9381 [4.9371; 4.9392] 4.4 1.5 HIC
## SWEDEN 3.2632 [3.2613; 3.2650] 0.6 1.5 HIC
## SWITZERLAND 3.5588 [3.5567; 3.5609] 0.6 1.5 HIC
## TAIWAN 0.3043 [0.3040; 0.3047] 0.1 1.5 HIC
## THAILAND 0.1857 [0.1856; 0.1859] 0.2 1.5 UMIC
## TUNISIA 0.7481 [0.7473; 0.7489] 0.2 1.5 LMIC
## TÜRKIYE 3.2735 [3.2728; 3.2741] 5.0 1.5 UMIC
## UNITED ARAB EMIRATES 0.6187 [0.6179; 0.6196] 0.1 1.5 HIC
## UNITED KINGDOM 7.4479 [7.4468; 7.4490] 9.4 1.5 HIC
## UNITED STATES 2.4957 [2.4955; 2.4960] 15.3 1.5 HIC
## URUGUAY 1.3737 [1.3716; 1.3757] 0.1 1.5 HIC
## VENEZUELA 0.3986 [0.3983; 0.3990] 0.2 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.4919 [2.4918; 2.4920] 40118.40 0
## Random effects model 0.9697 [0.7523; 1.2500] -0.24 0.8124
##
## Quantifying heterogeneity:
## tau^2 = 1.0907 [1.0071; 2.5640]; tau = 1.0444 [1.0035; 1.6012]
## I^2 = 100.0%; H = 5540.91
##
## Test of heterogeneity:
## Q d.f. p-value
## 1964908255.31 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 3.8480 [3.8478; 3.8482] 360072457.49 100.0%
## income = UMIC 21 1.2325 [1.2323; 1.2326] 388573789.48 100.0%
## income = LMIC 6 0.5246 [0.5245; 0.5246] 158174738.36 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1058087269.98 2 0
## Within groups 906820985.33 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.1440 [1.8085; 2.5418] 0.2865 0.5353
## income = UMIC 21 0.3087 [0.1713; 0.5564] 1.8971 1.3773
## income = LMIC 6 0.3501 [0.1514; 0.8094] 1.0972 1.0475
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 52.94 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 2.8536 [2.8528; 2.8544] 2.1 1.5 UMIC
## ARGENTINA 1.2643 [1.2638; 1.2649] 1.0 1.5 UMIC
## AUSTRALIA 8.7381 [8.7362; 8.7400] 3.7 1.5 HIC
## AUSTRIA 3.9874 [3.9852; 3.9895] 0.6 1.5 HIC
## BELARUS 0.0664 [0.0661; 0.0666] 0.0 1.5 UMIC
## BELGIUM 4.7493 [4.7472; 4.7514] 0.9 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1682 [0.1675; 0.1690] 0.0 1.5 UMIC
## BRAZIL 0.5116 [0.5115; 0.5118] 1.8 1.5 UMIC
## BULGARIA 0.9454 [0.9442; 0.9466] 0.1 1.5 UMIC
## CANADA 6.0709 [6.0696; 6.0722] 3.9 1.5 HIC
## CHILE 0.9066 [0.9059; 0.9073] 0.3 1.5 HIC
## CHINA 0.0108 [0.0108; 0.0108] 0.3 1.5 UMIC
## COLOMBIA 0.1810 [0.1808; 0.1812] 0.2 1.5 UMIC
## CROATIA 1.2599 [1.2581; 1.2616] 0.1 1.5 HIC
## CZECH REPUBLIC 3.9558 [3.9538; 3.9577] 0.7 1.5 HIC
## ECUADOR 0.4963 [0.4958; 0.4969] 0.1 1.5 UMIC
## EGYPT 3.7897 [3.7891; 3.7904] 6.4 1.5 LMIC
## ESTONIA 1.7170 [1.7133; 1.7207] 0.0 1.5 HIC
## FINLAND 5.1854 [5.1823; 5.1886] 0.5 1.5 HIC
## FRANCE 4.6663 [4.6654; 4.6671] 5.2 1.5 HIC
## GERMANY 3.9886 [3.9879; 3.9893] 5.7 1.5 HIC
## GREECE 3.6452 [3.6433; 3.6471] 0.7 1.5 HIC
## HUNGARY 1.3400 [1.3388; 1.3412] 0.2 1.5 HIC
## INDIA 0.4017 [0.4017; 0.4018] 9.3 1.5 LMIC
## IRELAND 7.2996 [7.2956; 7.3036] 0.6 1.5 HIC
## ITALY 2.3084 [2.3077; 2.3090] 2.4 1.5 HIC
## JAPAN 3.9854 [3.9848; 3.9859] 8.7 1.5 HIC
## JORDAN 0.3184 [0.3178; 0.3190] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0783 [0.0781; 0.0785] 0.0 1.5 UMIC
## KUWAIT 2.3085 [2.3061; 2.3110] 0.2 1.5 HIC
## LATVIA 0.2861 [0.2849; 0.2874] 0.0 1.5 HIC
## LEBANON 1.0700 [1.0688; 1.0713] 0.1 1.5 UMIC
## LITHUANIA 1.4162 [1.4139; 1.4185] 0.1 1.5 HIC
## LUXEMBOURG 3.7805 [3.7724; 3.7886] 0.0 1.5 HIC
## MEXICO 0.3123 [0.3121; 0.3124] 0.7 1.5 UMIC
## MOROCCO 0.1609 [0.1607; 0.1611] 0.1 1.5 LMIC
## NETHERLANDS 3.2409 [3.2395; 3.2423] 1.0 1.5 HIC
## NEW ZEALAND 1.0352 [1.0336; 1.0367] 0.1 1.5 HIC
## NORWAY 3.4372 [3.4346; 3.4398] 0.3 1.5 HIC
## PAKISTAN 0.3665 [0.3664; 0.3667] 1.3 1.5 LMIC
## PERU 0.1859 [0.1856; 0.1861] 0.1 1.5 UMIC
## PHILIPPINES 0.1276 [0.1275; 0.1277] 0.2 1.5 LMIC
## POLAND 1.1714 [1.1709; 1.1720] 0.8 1.5 HIC
## PORTUGAL 3.6716 [3.6697; 3.6735] 0.6 1.5 HIC
## PUERTO RICO 1.1985 [1.1965; 1.2006] 0.1 1.5 HIC
## ROMANIA 0.2448 [0.2444; 0.2451] 0.1 1.5 UMIC
## RUSSIA 0.2948 [0.2947; 0.2950] 0.7 1.5 UMIC
## SAUDI ARABIA 0.3547 [0.3544; 0.3550] 0.2 1.5 HIC
## SERBIA 1.8492 [1.8478; 1.8507] 0.3 1.5 UMIC
## SLOVAKIA 3.4508 [3.4482; 3.4534] 0.3 1.5 HIC
## SLOVENIA 3.6286 [3.6243; 3.6329] 0.1 1.5 HIC
## SOUTH AFRICA 0.3076 [0.3074; 0.3079] 0.3 1.5 UMIC
## SOUTH KOREA 2.1687 [2.1681; 2.1694] 1.9 1.5 HIC
## SPAIN 5.1063 [5.1052; 5.1073] 4.1 1.5 HIC
## SWEDEN 3.2313 [3.2294; 3.2331] 0.6 1.5 HIC
## SWITZERLAND 3.7111 [3.7090; 3.7132] 0.5 1.5 HIC
## TAIWAN 0.3825 [0.3821; 0.3829] 0.2 1.5 HIC
## THAILAND 0.2323 [0.2321; 0.2325] 0.3 1.5 UMIC
## TUNISIA 1.9419 [1.9406; 1.9433] 0.4 1.5 LMIC
## TÜRKIYE 3.5808 [3.5801; 3.5815] 5.1 1.5 UMIC
## UNITED ARAB EMIRATES 0.6074 [0.6066; 0.6082] 0.1 1.5 HIC
## UNITED KINGDOM 7.7862 [7.7851; 7.7873] 9.0 1.5 HIC
## UNITED STATES 2.5196 [2.5193; 2.5199] 14.2 1.5 HIC
## URUGUAY 1.2352 [1.2333; 1.2372] 0.1 1.5 HIC
## VENEZUELA 0.3925 [0.3921; 0.3928] 0.2 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.6286 [2.6285; 2.6287] 44545.79 0
## Random effects model 1.1191 [0.8710; 1.4379] 0.88 0.3788
##
## Quantifying heterogeneity:
## tau^2 = 1.0630 [0.9666; 2.4512]; tau = 1.0310 [0.9831; 1.5656]
## I^2 = 100.0%; H = 5745.66
##
## Test of heterogeneity:
## Q d.f. p-value
## 2112804127.48 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 4.0016 [4.0014; 4.0018] 368123512.47 100.0%
## income = UMIC 21 1.2615 [1.2614; 1.2617] 440708561.40 100.0%
## income = LMIC 6 0.9076 [0.9076; 0.9077] 463712996.34 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 840259057.27 2 0
## Within groups 1272545070.21 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.3694 [2.0020; 2.8042] 0.2808 0.5299
## income = UMIC 21 0.3565 [0.1974; 0.6438] 1.9093 1.3818
## income = LMIC 6 0.5303 [0.1672; 1.6818] 2.0805 1.4424
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 41.68 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0709 [0.0708; 0.0711] 0.1 1.7 UMIC
## ARGENTINA 0.2110 [0.2108; 0.2113] 0.2 1.7 UMIC
## AUSTRALIA 1.2865 [1.2857; 1.2873] 0.7 1.7 HIC
## AUSTRIA 2.1978 [2.1961; 2.1995] 0.5 1.7 HIC
## BELARUS 0.0056 [0.0055; 0.0057] 0.0 1.7 UMIC
## BELGIUM 1.3012 [1.3001; 1.3024] 0.4 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0522 [0.0522; 0.0523] 0.3 1.7 UMIC
## BULGARIA 0.1053 [0.1049; 0.1057] 0.0 1.7 UMIC
## CANADA 4.1266 [4.1254; 4.1277] 3.6 1.7 HIC
## CHILE 0.2113 [0.2110; 0.2117] 0.1 1.7 HIC
## CHINA 0.0009 [0.0009; 0.0009] 0.0 1.7 UMIC
## COLOMBIA 0.0876 [0.0874; 0.0877] 0.1 1.7 UMIC
## CROATIA 0.3160 [0.3151; 0.3168] 0.0 1.7 HIC
## CZECH REPUBLIC 1.1416 [1.1405; 1.1427] 0.3 1.7 HIC
## ECUADOR 0.1948 [0.1944; 0.1951] 0.1 1.7 UMIC
## EGYPT 0.1064 [0.1063; 0.1066] 0.2 1.7 LMIC
## ESTONIA 0.2020 [0.2007; 0.2032] 0.0 1.7 HIC
## FINLAND 4.3558 [4.3528; 4.3587] 0.6 1.7 HIC
## FRANCE 3.5466 [3.5458; 3.5473] 5.8 1.7 HIC
## GERMANY 2.7848 [2.7842; 2.7854] 6.0 1.7 HIC
## GREECE 2.2484 [2.2470; 2.2499] 0.7 1.7 HIC
## HUNGARY 0.7213 [0.7204; 0.7221] 0.2 1.7 HIC
## INDIA 0.1154 [0.1154; 0.1154] 3.7 1.7 LMIC
## IRELAND 2.8727 [2.8700; 2.8753] 0.3 1.7 HIC
## ITALY 1.4918 [1.4913; 1.4923] 2.3 1.7 HIC
## JAPAN 0.1016 [0.1015; 0.1017] 0.3 1.7 HIC
## JORDAN 0.1265 [0.1261; 0.1270] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1175 [0.1168; 0.1182] 0.0 1.7 HIC
## LATVIA 0.4164 [0.4150; 0.4178] 0.0 1.7 HIC
## LEBANON 0.4021 [0.4012; 0.4031] 0.1 1.7 UMIC
## LITHUANIA 0.3469 [0.3459; 0.3480] 0.0 1.7 HIC
## LUXEMBOURG 3.2514 [3.2430; 3.2598] 0.0 1.7 HIC
## MEXICO 0.2788 [0.2786; 0.2789] 0.8 1.7 UMIC
## MOROCCO 0.0236 [0.0235; 0.0237] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.9869 [0.9853; 0.9885] 0.1 1.7 HIC
## NORWAY 3.4299 [3.4272; 3.4327] 0.4 1.7 HIC
## PAKISTAN 0.0973 [0.0972; 0.0974] 0.4 1.7 LMIC
## PERU 0.0497 [0.0495; 0.0498] 0.0 1.7 UMIC
## PHILIPPINES 0.0530 [0.0529; 0.0531] 0.1 1.7 LMIC
## POLAND 0.1513 [0.1511; 0.1515] 0.2 1.7 HIC
## PORTUGAL 3.1225 [3.1207; 3.1243] 0.9 1.7 HIC
## PUERTO RICO 5.0091 [5.0053; 5.0129] 0.5 1.7 HIC
## ROMANIA 0.3109 [0.3105; 0.3113] 0.2 1.7 UMIC
## RUSSIA 0.0289 [0.0289; 0.0290] 0.1 1.7 UMIC
## SAUDI ARABIA 0.3616 [0.3612; 0.3620] 0.2 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.4112 [1.4096; 1.4129] 0.2 1.7 HIC
## SLOVENIA 1.5309 [1.5280; 1.5337] 0.1 1.7 HIC
## SOUTH AFRICA 0.1159 [0.1158; 0.1161] 0.2 1.7 UMIC
## SOUTH KOREA 0.9479 [0.9475; 0.9484] 1.2 1.7 HIC
## SPAIN 3.8350 [3.8340; 3.8359] 4.7 1.7 HIC
## SWEDEN 3.3977 [3.3958; 3.3997] 0.8 1.7 HIC
## SWITZERLAND 1.9623 [1.9607; 1.9640] 0.4 1.7 HIC
## TAIWAN 0.1750 [0.1747; 0.1753] 0.1 1.7 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.1223 [0.1220; 0.1227] 0.0 1.7 LMIC
## TÜRKIYE 1.3869 [1.3864; 1.3873] 2.6 1.7 UMIC
## UNITED ARAB EMIRATES 0.3101 [0.3094; 0.3108] 0.1 1.7 HIC
## UNITED KINGDOM 3.0456 [3.0449; 3.0463] 5.0 1.7 HIC
## UNITED STATES 6.6631 [6.6626; 6.6636] 53.4 1.7 HIC
## URUGUAY 0.2539 [0.2530; 0.2548] 0.0 1.7 HIC
## VENEZUELA 0.6793 [0.6788; 0.6798] 0.5 1.7 UMIC
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.4362 [3.4360; 3.4364] 45911.48 0
## Random effects model 0.4167 [0.2989; 0.5810] -5.16 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.7246 [1.4813; 4.5594]; tau = 1.3132 [1.2171; 2.1353]
## I^2 = 100.0%; H = 5315.64
##
## Test of heterogeneity:
## Q d.f. p-value
## 1667108391.00 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 4.5536 [4.5533; 4.5538] 456103261.99 100.0%
## income = UMIC 17 0.5143 [0.5141; 0.5144] 111270810.72 100.0%
## income = LMIC 6 0.1099 [0.1098; 0.1099] 1798505.85 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1097935812.45 2 0
## Within groups 569172578.55 57 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.0789 [0.8447; 1.3781] 0.5769 0.7596
## income = UMIC 17 0.0961 [0.0478; 0.1932] 2.1566 1.4685
## income = LMIC 6 0.0753 [0.0594; 0.0955] 0.0882 0.2970
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 242.03 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.2753 [0.2750; 0.2756] 0.2 1.7 UMIC
## ARGENTINA 0.2916 [0.2913; 0.2918] 0.3 1.7 UMIC
## AUSTRALIA 1.4792 [1.4784; 1.4801] 0.7 1.7 HIC
## AUSTRIA 2.5392 [2.5375; 2.5410] 0.5 1.7 HIC
## BELARUS 0.0141 [0.0139; 0.0142] 0.0 1.7 UMIC
## BELGIUM 1.5782 [1.5770; 1.5795] 0.4 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0739 [0.0739; 0.0740] 0.3 1.7 UMIC
## BULGARIA 0.2261 [0.2255; 0.2267] 0.0 1.7 UMIC
## CANADA 4.7887 [4.7875; 4.7899] 3.7 1.7 HIC
## CHILE 0.2263 [0.2259; 0.2266] 0.1 1.7 HIC
## CHINA 0.0020 [0.0020; 0.0020] 0.1 1.7 UMIC
## COLOMBIA 0.0840 [0.0839; 0.0841] 0.1 1.7 UMIC
## CROATIA 0.3611 [0.3602; 0.3621] 0.0 1.7 HIC
## CZECH REPUBLIC 1.4811 [1.4799; 1.4823] 0.4 1.7 HIC
## ECUADOR 0.2420 [0.2416; 0.2424] 0.1 1.7 UMIC
## EGYPT 0.1499 [0.1497; 0.1500] 0.3 1.7 LMIC
## ESTONIA 0.2543 [0.2529; 0.2557] 0.0 1.7 HIC
## FINLAND 4.9666 [4.9635; 4.9698] 0.6 1.7 HIC
## FRANCE 3.8551 [3.8543; 3.8559] 5.5 1.7 HIC
## GERMANY 3.2166 [3.2159; 3.2172] 5.9 1.7 HIC
## GREECE 2.6236 [2.6221; 2.6252] 0.7 1.7 HIC
## HUNGARY 0.8353 [0.8344; 0.8363] 0.2 1.7 HIC
## INDIA 0.1569 [0.1569; 0.1569] 4.4 1.7 LMIC
## IRELAND 3.5075 [3.5046; 3.5103] 0.4 1.7 HIC
## ITALY 1.6554 [1.6548; 1.6559] 2.2 1.7 HIC
## JAPAN 0.1478 [0.1477; 0.1479] 0.4 1.7 HIC
## JORDAN 0.1551 [0.1546; 0.1556] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1514 [0.1507; 0.1522] 0.0 1.7 HIC
## LATVIA 0.5005 [0.4989; 0.5021] 0.0 1.7 HIC
## LEBANON 0.5855 [0.5844; 0.5867] 0.1 1.7 UMIC
## LITHUANIA 0.4040 [0.4028; 0.4051] 0.0 1.7 HIC
## LUXEMBOURG 3.3859 [3.3774; 3.3943] 0.0 1.7 HIC
## MEXICO 0.2783 [0.2782; 0.2785] 0.7 1.7 UMIC
## MOROCCO 0.0386 [0.0385; 0.0387] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.1928 [1.1911; 1.1945] 0.1 1.7 HIC
## NORWAY 3.6102 [3.6074; 3.6130] 0.4 1.7 HIC
## PAKISTAN 0.1376 [0.1375; 0.1377] 0.6 1.7 LMIC
## PERU 0.0548 [0.0547; 0.0549] 0.0 1.7 UMIC
## PHILIPPINES 0.0637 [0.0636; 0.0638] 0.1 1.7 LMIC
## POLAND 0.1745 [0.1743; 0.1747] 0.2 1.7 HIC
## PORTUGAL 3.3821 [3.3803; 3.3840] 0.8 1.7 HIC
## PUERTO RICO 5.9562 [5.9520; 5.9603] 0.5 1.7 HIC
## ROMANIA 0.5838 [0.5832; 0.5843] 0.3 1.7 UMIC
## RUSSIA 0.0450 [0.0449; 0.0451] 0.1 1.7 UMIC
## SAUDI ARABIA 0.4092 [0.4088; 0.4096] 0.2 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.8496 [1.8477; 1.8515] 0.2 1.7 HIC
## SLOVENIA 1.7773 [1.7743; 1.7803] 0.1 1.7 HIC
## SOUTH AFRICA 0.1586 [0.1584; 0.1588] 0.2 1.7 UMIC
## SOUTH KOREA 1.1294 [1.1289; 1.1299] 1.3 1.7 HIC
## SPAIN 4.4325 [4.4315; 4.4335] 4.7 1.7 HIC
## SWEDEN 3.9503 [3.9482; 3.9524] 0.8 1.7 HIC
## SWITZERLAND 2.1416 [2.1399; 2.1433] 0.4 1.7 HIC
## TAIWAN 0.1779 [0.1776; 0.1781] 0.1 1.7 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.2006 [0.2002; 0.2011] 0.0 1.7 LMIC
## TÜRKIYE 1.7331 [1.7326; 1.7337] 2.8 1.7 UMIC
## UNITED ARAB EMIRATES 0.4496 [0.4488; 0.4503] 0.1 1.7 HIC
## UNITED KINGDOM 3.8182 [3.8174; 3.8190] 5.5 1.7 HIC
## UNITED STATES 7.4008 [7.4003; 7.4013] 51.6 1.7 HIC
## URUGUAY 0.4441 [0.4430; 0.4453] 0.0 1.7 HIC
## VENEZUELA 0.7331 [0.7326; 0.7337] 0.5 1.7 UMIC
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.7177 [3.7175; 3.7178] 52578.79 0
## Random effects model 0.5392 [0.3863; 0.7527] -3.63 0.0003
##
## Quantifying heterogeneity:
## tau^2 = 1.7372 [1.4181; 4.2743]; tau = 1.3180 [1.1909; 2.0674]
## I^2 = 100.0%; H = 5815.15
##
## Test of heterogeneity:
## Q d.f. p-value
## 1995142807.78 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 5.0846 [5.0843; 5.0849] 508514702.66 100.0%
## income = UMIC 17 0.6094 [0.6092; 0.6095] 149931492.76 100.0%
## income = LMIC 6 0.1502 [0.1502; 0.1503] 2678386.85 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1334018225.51 2 0
## Within groups 661124582.27 57 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.2793 [1.0063; 1.6263] 0.5548 0.7448
## income = UMIC 17 0.1450 [0.0717; 0.2934] 2.1967 1.4821
## income = LMIC 6 0.1081 [0.0847; 0.1379] 0.0928 0.3046
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 207.25 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.3584 [0.3581; 0.3587] 0.3 1.6 UMIC
## ARGENTINA 0.4124 [0.4120; 0.4127] 0.3 1.6 UMIC
## AUSTRALIA 1.6897 [1.6888; 1.6906] 0.7 1.6 HIC
## AUSTRIA 3.0211 [3.0192; 3.0231] 0.5 1.6 HIC
## BELARUS 0.0180 [0.0178; 0.0181] 0.0 1.6 UMIC
## BELGIUM 2.2085 [2.2071; 2.2100] 0.5 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.1212 [0.1211; 0.1213] 0.5 1.6 UMIC
## BULGARIA 0.3263 [0.3256; 0.3270] 0.0 1.6 UMIC
## CANADA 5.3477 [5.3464; 5.3490] 3.5 1.6 HIC
## CHILE 0.2580 [0.2576; 0.2584] 0.1 1.6 HIC
## CHINA 0.0031 [0.0031; 0.0031] 0.1 1.6 UMIC
## COLOMBIA 0.0760 [0.0759; 0.0762] 0.1 1.6 UMIC
## CROATIA 0.4543 [0.4532; 0.4553] 0.0 1.6 HIC
## CZECH REPUBLIC 1.8185 [1.8172; 1.8199] 0.4 1.6 HIC
## ECUADOR 0.2917 [0.2912; 0.2921] 0.1 1.6 UMIC
## EGYPT 0.2397 [0.2396; 0.2399] 0.4 1.6 LMIC
## ESTONIA 0.4373 [0.4355; 0.4392] 0.0 1.6 HIC
## FINLAND 5.4352 [5.4319; 5.4384] 0.6 1.6 HIC
## FRANCE 4.1757 [4.1748; 4.1765] 5.1 1.6 HIC
## GERMANY 3.5994 [3.5987; 3.6001] 5.7 1.6 HIC
## GREECE 2.8252 [2.8236; 2.8269] 0.6 1.6 HIC
## HUNGARY 1.0156 [1.0146; 1.0167] 0.2 1.6 HIC
## INDIA 0.1918 [0.1918; 0.1919] 4.6 1.6 LMIC
## IRELAND 3.9361 [3.9331; 3.9391] 0.3 1.6 HIC
## ITALY 1.8485 [1.8479; 1.8491] 2.1 1.6 HIC
## JAPAN 0.3150 [0.3149; 0.3152] 0.8 1.6 HIC
## JORDAN 0.1833 [0.1828; 0.1838] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1562 [0.1555; 0.1569] 0.0 1.6 HIC
## LATVIA 0.5646 [0.5630; 0.5663] 0.0 1.6 HIC
## LEBANON 0.6460 [0.6448; 0.6471] 0.1 1.6 UMIC
## LITHUANIA 0.5072 [0.5059; 0.5085] 0.0 1.6 HIC
## LUXEMBOURG 3.6404 [3.6317; 3.6491] 0.0 1.6 HIC
## MEXICO 0.2904 [0.2902; 0.2906] 0.6 1.6 UMIC
## MOROCCO 0.0432 [0.0431; 0.0433] 0.0 1.6 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.4364 [1.4345; 1.4383] 0.1 1.6 HIC
## NORWAY 3.9539 [3.9510; 3.9568] 0.4 1.6 HIC
## PAKISTAN 0.1573 [0.1572; 0.1574] 0.5 1.6 LMIC
## PERU 0.0700 [0.0698; 0.0702] 0.0 1.6 UMIC
## PHILIPPINES 0.0686 [0.0685; 0.0687] 0.1 1.6 LMIC
## POLAND 0.2009 [0.2007; 0.2012] 0.1 1.6 HIC
## PORTUGAL 3.8120 [3.8101; 3.8140] 0.8 1.6 HIC
## PUERTO RICO 6.7601 [6.7556; 6.7645] 0.5 1.6 HIC
## ROMANIA 0.5874 [0.5868; 0.5879] 0.2 1.6 UMIC
## RUSSIA 0.0884 [0.0883; 0.0885] 0.2 1.6 UMIC
## SAUDI ARABIA 0.4869 [0.4865; 0.4873] 0.3 1.6 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 2.3771 [2.3750; 2.3793] 0.2 1.6 HIC
## SLOVENIA 2.0489 [2.0457; 2.0522] 0.1 1.6 HIC
## SOUTH AFRICA 0.1825 [0.1823; 0.1827] 0.2 1.6 UMIC
## SOUTH KOREA 1.2561 [1.2556; 1.2566] 1.2 1.6 HIC
## SPAIN 4.8978 [4.8967; 4.8988] 4.5 1.6 HIC
## SWEDEN 4.3114 [4.3092; 4.3136] 0.8 1.6 HIC
## SWITZERLAND 2.4739 [2.4721; 2.4758] 0.4 1.6 HIC
## TAIWAN 0.1970 [0.1967; 0.1973] 0.1 1.6 HIC
## THAILAND 0.2391 [0.2389; 0.2393] 0.3 1.6 UMIC
## TUNISIA 0.2414 [0.2409; 0.2418] 0.0 1.6 LMIC
## TÜRKIYE 2.1626 [2.1620; 2.1632] 3.0 1.6 UMIC
## UNITED ARAB EMIRATES 0.7453 [0.7443; 0.7462] 0.1 1.6 HIC
## UNITED KINGDOM 4.7452 [4.7443; 4.7461] 5.9 1.6 HIC
## UNITED STATES 8.5063 [8.5057; 8.5068] 51.1 1.6 HIC
## URUGUAY 0.5483 [0.5470; 0.5496] 0.0 1.6 HIC
## VENEZUELA 0.7677 [0.7672; 0.7682] 0.4 1.6 UMIC
##
## Number of studies combined: k = 61
##
## rate 95%-CI z p-value
## Common effect model 4.1189 [4.1187; 4.1191] 61374.22 0
## Random effects model 0.6453 [0.4606; 0.9041] -2.55 0.0109
##
## Quantifying heterogeneity:
## tau^2 = 1.8051 [1.3849; 4.0849]; tau = 1.3435 [1.1768; 2.0211]
## I^2 = 100.0%; H = 6391.62
##
## Test of heterogeneity:
## Q d.f. p-value
## 2451167882.77 60 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 5.7706 [5.7703; 5.7709] 618378082.54 100.0%
## income = UMIC 18 0.6759 [0.6757; 0.6760] 204644719.24 100.0%
## income = LMIC 6 0.1859 [0.1859; 0.1859] 4332217.37 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1623812863.63 2 0
## Within groups 827355019.15 58 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.5329 [1.1986; 1.9604] 0.5827 0.7634
## income = UMIC 18 0.1852 [0.0932; 0.3680] 2.2093 1.4864
## income = LMIC 6 0.1315 [0.1000; 0.1730] 0.1172 0.3424
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 178.76 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5023 [0.5019; 0.5026] 0.3 1.5 UMIC
## ARGENTINA 0.6236 [0.6232; 0.6240] 0.4 1.5 UMIC
## AUSTRALIA 1.9091 [1.9081; 1.9100] 0.7 1.5 HIC
## AUSTRIA 3.4460 [3.4440; 3.4481] 0.5 1.5 HIC
## BELARUS 0.0188 [0.0187; 0.0190] 0.0 1.5 UMIC
## BELGIUM 2.6343 [2.6327; 2.6359] 0.5 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0490 [0.0486; 0.0494] 0.0 1.5 UMIC
## BRAZIL 0.1618 [0.1617; 0.1619] 0.6 1.5 UMIC
## BULGARIA 0.4892 [0.4884; 0.4901] 0.1 1.5 UMIC
## CANADA 5.8395 [5.8381; 5.8408] 3.5 1.5 HIC
## CHILE 0.3141 [0.3137; 0.3146] 0.1 1.5 HIC
## CHINA 0.0053 [0.0053; 0.0053] 0.1 1.5 UMIC
## COLOMBIA 0.0820 [0.0819; 0.0821] 0.1 1.5 UMIC
## CROATIA 0.4773 [0.4762; 0.4783] 0.0 1.5 HIC
## CZECH REPUBLIC 2.2432 [2.2417; 2.2447] 0.4 1.5 HIC
## ECUADOR 0.3322 [0.3317; 0.3327] 0.1 1.5 UMIC
## EGYPT 0.3250 [0.3248; 0.3252] 0.5 1.5 LMIC
## ESTONIA 0.5211 [0.5191; 0.5231] 0.0 1.5 HIC
## FINLAND 5.7890 [5.7856; 5.7923] 0.5 1.5 HIC
## FRANCE 4.3160 [4.3151; 4.3168] 4.7 1.5 HIC
## GERMANY 3.8865 [3.8858; 3.8872] 5.4 1.5 HIC
## GREECE 3.0761 [3.0744; 3.0778] 0.6 1.5 HIC
## HUNGARY 1.2037 [1.2026; 1.2048] 0.2 1.5 HIC
## INDIA 0.2357 [0.2356; 0.2357] 5.1 1.5 LMIC
## IRELAND 4.6899 [4.6866; 4.6932] 0.4 1.5 HIC
## ITALY 2.0384 [2.0378; 2.0390] 2.1 1.5 HIC
## JAPAN 1.0988 [1.0985; 1.0991] 2.4 1.5 HIC
## JORDAN 0.2052 [0.2047; 0.2057] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0285 [0.0283; 0.0286] 0.0 1.5 UMIC
## KUWAIT 0.2263 [0.2254; 0.2271] 0.0 1.5 HIC
## LATVIA 0.6788 [0.6770; 0.6807] 0.0 1.5 HIC
## LEBANON 0.7612 [0.7600; 0.7625] 0.1 1.5 UMIC
## LITHUANIA 0.7232 [0.7217; 0.7248] 0.0 1.5 HIC
## LUXEMBOURG 3.7951 [3.7863; 3.8039] 0.0 1.5 HIC
## MEXICO 0.2666 [0.2664; 0.2668] 0.5 1.5 UMIC
## MOROCCO 0.0533 [0.0531; 0.0534] 0.0 1.5 LMIC
## NETHERLANDS 2.5304 [2.5291; 2.5317] 0.7 1.5 HIC
## NEW ZEALAND 1.6623 [1.6603; 1.6643] 0.1 1.5 HIC
## NORWAY 4.4844 [4.4813; 4.4874] 0.4 1.5 HIC
## PAKISTAN 0.1948 [0.1947; 0.1949] 0.6 1.5 LMIC
## PERU 0.0813 [0.0811; 0.0815] 0.0 1.5 UMIC
## PHILIPPINES 0.0735 [0.0734; 0.0736] 0.1 1.5 LMIC
## POLAND 0.2380 [0.2377; 0.2383] 0.2 1.5 HIC
## PORTUGAL 3.8753 [3.8734; 3.8773] 0.7 1.5 HIC
## PUERTO RICO 8.4500 [8.4450; 8.4550] 0.5 1.5 HIC
## ROMANIA 0.7133 [0.7126; 0.7139] 0.3 1.5 UMIC
## RUSSIA 0.1598 [0.1597; 0.1599] 0.4 1.5 UMIC
## SAUDI ARABIA 0.5929 [0.5924; 0.5933] 0.3 1.5 HIC
## SERBIA 0.0947 [0.0944; 0.0950] 0.0 1.5 UMIC
## SLOVAKIA 2.9752 [2.9728; 2.9776] 0.3 1.5 HIC
## SLOVENIA 2.2320 [2.2286; 2.2354] 0.1 1.5 HIC
## SOUTH AFRICA 0.2093 [0.2091; 0.2095] 0.2 1.5 UMIC
## SOUTH KOREA 1.3683 [1.3677; 1.3688] 1.2 1.5 HIC
## SPAIN 5.4072 [5.4061; 5.4083] 4.4 1.5 HIC
## SWEDEN 4.6491 [4.6469; 4.6514] 0.8 1.5 HIC
## SWITZERLAND 2.7352 [2.7333; 2.7371] 0.4 1.5 HIC
## TAIWAN 0.2174 [0.2171; 0.2177] 0.1 1.5 HIC
## THAILAND 0.3580 [0.3578; 0.3583] 0.4 1.5 UMIC
## TUNISIA 0.2845 [0.2840; 0.2851] 0.1 1.5 LMIC
## TÜRKIYE 2.7223 [2.7216; 2.7229] 3.5 1.5 UMIC
## UNITED ARAB EMIRATES 0.8151 [0.8141; 0.8160] 0.1 1.5 HIC
## UNITED KINGDOM 5.6548 [5.6539; 5.6558] 6.3 1.5 HIC
## UNITED STATES 8.7504 [8.7498; 8.7509] 47.2 1.5 HIC
## URUGUAY 0.6987 [0.6973; 0.7002] 0.0 1.5 HIC
## VENEZUELA 0.8805 [0.8799; 0.8810] 0.4 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.1030 [4.1028; 4.1031] 64828.80 0
## Random effects model 0.7013 [0.5117; 0.9613] -2.21 0.0274
##
## Quantifying heterogeneity:
## tau^2 = 1.6824 [1.3038; 3.6877]; tau = 1.2971 [1.1418; 1.9203]
## I^2 = 100.0%; H = 6486.87
##
## Test of heterogeneity:
## Q d.f. p-value
## 2693089052.56 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 5.8503 [5.8501; 5.8506] 652120118.32 100.0%
## income = UMIC 21 0.8204 [0.8203; 0.8205] 275928723.50 100.0%
## income = LMIC 6 0.2305 [0.2305; 0.2306] 6377539.55 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1758662671.18 2 0
## Within groups 934426381.37 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.8301 [1.4525; 2.3058] 0.5281 0.7267
## income = UMIC 21 0.1887 [0.0988; 0.3603] 2.2881 1.5127
## income = LMIC 6 0.1597 [0.1190; 0.2144] 0.1352 0.3677
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 178.54 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5436 [0.5432; 0.5440] 0.3 1.5 UMIC
## ARGENTINA 0.8075 [0.8071; 0.8080] 0.5 1.5 UMIC
## AUSTRALIA 2.1429 [2.1419; 2.1439] 0.8 1.5 HIC
## AUSTRIA 3.8892 [3.8870; 3.8914] 0.5 1.5 HIC
## BELARUS 0.0209 [0.0208; 0.0211] 0.0 1.5 UMIC
## BELGIUM 2.8197 [2.8180; 2.8213] 0.5 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1042 [0.1036; 0.1047] 0.0 1.5 UMIC
## BRAZIL 0.2003 [0.2002; 0.2004] 0.6 1.5 UMIC
## BULGARIA 0.3512 [0.3505; 0.3520] 0.0 1.5 UMIC
## CANADA 6.4490 [6.4476; 6.4504] 3.6 1.5 HIC
## CHILE 0.4029 [0.4024; 0.4033] 0.1 1.5 HIC
## CHINA 0.0100 [0.0100; 0.0101] 0.2 1.5 UMIC
## COLOMBIA 0.0904 [0.0902; 0.0905] 0.1 1.5 UMIC
## CROATIA 0.5290 [0.5279; 0.5302] 0.0 1.5 HIC
## CZECH REPUBLIC 2.4462 [2.4447; 2.4478] 0.4 1.5 HIC
## ECUADOR 0.3715 [0.3710; 0.3720] 0.1 1.5 UMIC
## EGYPT 0.4519 [0.4517; 0.4522] 0.6 1.5 LMIC
## ESTONIA 0.6499 [0.6477; 0.6522] 0.0 1.5 HIC
## FINLAND 6.2340 [6.2305; 6.2375] 0.5 1.5 HIC
## FRANCE 4.6399 [4.6391; 4.6408] 4.8 1.5 HIC
## GERMANY 4.1898 [4.1890; 4.1905] 5.5 1.5 HIC
## GREECE 2.9461 [2.9444; 2.9478] 0.5 1.5 HIC
## HUNGARY 1.3818 [1.3806; 1.3831] 0.2 1.5 HIC
## INDIA 0.2661 [0.2661; 0.2662] 5.4 1.5 LMIC
## IRELAND 5.7487 [5.7451; 5.7523] 0.4 1.5 HIC
## ITALY 2.1488 [2.1482; 2.1494] 2.1 1.5 HIC
## JAPAN 1.7888 [1.7885; 1.7892] 3.7 1.5 HIC
## JORDAN 0.2711 [0.2705; 0.2717] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0437 [0.0436; 0.0439] 0.0 1.5 UMIC
## KUWAIT 0.3421 [0.3411; 0.3431] 0.0 1.5 HIC
## LATVIA 0.8875 [0.8853; 0.8896] 0.0 1.5 HIC
## LEBANON 0.7933 [0.7921; 0.7945] 0.1 1.5 UMIC
## LITHUANIA 0.8457 [0.8440; 0.8474] 0.0 1.5 HIC
## LUXEMBOURG 4.0584 [4.0494; 4.0674] 0.0 1.5 HIC
## MEXICO 0.2671 [0.2669; 0.2672] 0.5 1.5 UMIC
## MOROCCO 0.0653 [0.0651; 0.0654] 0.0 1.5 LMIC
## NETHERLANDS 2.8475 [2.8461; 2.8488] 0.8 1.5 HIC
## NEW ZEALAND 1.9467 [1.9446; 1.9489] 0.1 1.5 HIC
## NORWAY 4.8179 [4.8147; 4.8211] 0.4 1.5 HIC
## PAKISTAN 0.2124 [0.2123; 0.2125] 0.6 1.5 LMIC
## PERU 0.0954 [0.0952; 0.0955] 0.0 1.5 UMIC
## PHILIPPINES 0.0780 [0.0779; 0.0781] 0.1 1.5 LMIC
## POLAND 0.2180 [0.2177; 0.2182] 0.1 1.5 HIC
## PORTUGAL 4.3551 [4.3530; 4.3572] 0.7 1.5 HIC
## PUERTO RICO 9.4847 [9.4794; 9.4900] 0.5 1.5 HIC
## ROMANIA 0.7460 [0.7453; 0.7466] 0.2 1.5 UMIC
## RUSSIA 0.3209 [0.3208; 0.3211] 0.7 1.5 UMIC
## SAUDI ARABIA 0.7378 [0.7373; 0.7383] 0.3 1.5 HIC
## SERBIA 0.1341 [0.1337; 0.1345] 0.0 1.5 UMIC
## SLOVAKIA 3.2284 [3.2259; 3.2309] 0.3 1.5 HIC
## SLOVENIA 2.4452 [2.4416; 2.4487] 0.1 1.5 HIC
## SOUTH AFRICA 0.2379 [0.2377; 0.2381] 0.2 1.5 UMIC
## SOUTH KOREA 1.7305 [1.7299; 1.7311] 1.4 1.5 HIC
## SPAIN 5.7247 [5.7236; 5.7258] 4.4 1.5 HIC
## SWEDEN 4.7299 [4.7276; 4.7322] 0.7 1.5 HIC
## SWITZERLAND 2.9603 [2.9584; 2.9623] 0.4 1.5 HIC
## TAIWAN 0.2224 [0.2221; 0.2227] 0.1 1.5 HIC
## THAILAND 0.4123 [0.4121; 0.4126] 0.5 1.5 UMIC
## TUNISIA 0.4017 [0.4011; 0.4024] 0.1 1.5 LMIC
## TÜRKIYE 3.1476 [3.1470; 3.1483] 3.8 1.5 UMIC
## UNITED ARAB EMIRATES 0.3576 [0.3569; 0.3582] 0.1 1.5 HIC
## UNITED KINGDOM 6.6897 [6.6887; 6.6908] 7.0 1.5 HIC
## UNITED STATES 8.4694 [8.4688; 8.4699] 43.0 1.5 HIC
## URUGUAY 1.0938 [1.0920; 1.0957] 0.1 1.5 HIC
## VENEZUELA 1.0497 [1.0491; 1.0503] 0.5 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.0158 [4.0156; 4.0160] 66064.07 0
## Random effects model 0.8148 [0.6018; 1.1031] -1.33 0.1851
##
## Quantifying heterogeneity:
## tau^2 = 1.5528 [1.2465; 3.4623]; tau = 1.2461 [1.1165; 1.8607]
## I^2 = 100.0%; H = 6597.00
##
## Test of heterogeneity:
## Q d.f. p-value
## 2785305996.51 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 5.8418 [5.8415; 5.8420] 577390760.27 100.0%
## income = UMIC 21 0.9048 [0.9047; 0.9049] 344302954.88 100.0%
## income = LMIC 6 0.2668 [0.2668; 0.2669] 10742914.12 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1852869367.25 2 0
## Within groups 932436629.26 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.0377 [1.6576; 2.5048] 0.4215 0.6492
## income = UMIC 21 0.2340 [0.1224; 0.4472] 2.2938 1.5145
## income = LMIC 6 0.1934 [0.1370; 0.2730] 0.1857 0.4309
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 152.20 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.6123 [0.6119; 0.6128] 0.3 1.5 UMIC
## ARGENTINA 0.9655 [0.9650; 0.9660] 0.6 1.5 UMIC
## AUSTRALIA 3.9071 [3.9058; 3.9084] 1.3 1.5 HIC
## AUSTRIA 4.2035 [4.2012; 4.2058] 0.5 1.5 HIC
## BELARUS 0.0344 [0.0342; 0.0346] 0.0 1.5 UMIC
## BELGIUM 2.8712 [2.8696; 2.8729] 0.4 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1485 [0.1479; 0.1492] 0.0 1.5 UMIC
## BRAZIL 0.2464 [0.2463; 0.2465] 0.7 1.5 UMIC
## BULGARIA 0.4812 [0.4804; 0.4820] 0.0 1.5 UMIC
## CANADA 7.3987 [7.3972; 7.4002] 3.7 1.5 HIC
## CHILE 0.4888 [0.4882; 0.4893] 0.1 1.5 HIC
## CHINA 0.0143 [0.0143; 0.0143] 0.3 1.5 UMIC
## COLOMBIA 0.1060 [0.1058; 0.1062] 0.1 1.5 UMIC
## CROATIA 0.5611 [0.5599; 0.5622] 0.0 1.5 HIC
## CZECH REPUBLIC 2.8554 [2.8537; 2.8570] 0.4 1.5 HIC
## ECUADOR 0.3806 [0.3801; 0.3811] 0.1 1.5 UMIC
## EGYPT 0.5793 [0.5791; 0.5796] 0.7 1.5 LMIC
## ESTONIA 0.8479 [0.8453; 0.8505] 0.0 1.5 HIC
## FINLAND 6.7770 [6.7733; 6.7806] 0.5 1.5 HIC
## FRANCE 4.9101 [4.9092; 4.9110] 4.4 1.5 HIC
## GERMANY 4.4645 [4.4637; 4.4653] 5.1 1.5 HIC
## GREECE 3.2543 [3.2525; 3.2561] 0.5 1.5 HIC
## HUNGARY 1.6067 [1.6054; 1.6080] 0.2 1.5 HIC
## INDIA 0.2922 [0.2921; 0.2922] 5.2 1.5 LMIC
## IRELAND 6.9816 [6.9776; 6.9855] 0.5 1.5 HIC
## ITALY 2.2892 [2.2886; 2.2899] 1.9 1.5 HIC
## JAPAN 2.3349 [2.3344; 2.3353] 4.2 1.5 HIC
## JORDAN 0.4185 [0.4177; 0.4192] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0488 [0.0487; 0.0490] 0.0 1.5 UMIC
## KUWAIT 0.7306 [0.7291; 0.7321] 0.0 1.5 HIC
## LATVIA 1.1425 [1.1401; 1.1449] 0.0 1.5 HIC
## LEBANON 0.8309 [0.8297; 0.8321] 0.1 1.5 UMIC
## LITHUANIA 1.0202 [1.0183; 1.0221] 0.0 1.5 HIC
## LUXEMBOURG 4.0818 [4.0729; 4.0907] 0.0 1.5 HIC
## MEXICO 0.2726 [0.2724; 0.2727] 0.5 1.5 UMIC
## MOROCCO 0.0781 [0.0780; 0.0783] 0.0 1.5 LMIC
## NETHERLANDS 3.0818 [3.0804; 3.0832] 0.7 1.5 HIC
## NEW ZEALAND 2.2133 [2.2110; 2.2155] 0.1 1.5 HIC
## NORWAY 4.9931 [4.9899; 4.9963] 0.4 1.5 HIC
## PAKISTAN 0.2292 [0.2291; 0.2293] 0.6 1.5 LMIC
## PERU 0.1101 [0.1099; 0.1103] 0.0 1.5 UMIC
## PHILIPPINES 0.0914 [0.0913; 0.0915] 0.1 1.5 LMIC
## POLAND 0.2820 [0.2817; 0.2822] 0.2 1.5 HIC
## PORTUGAL 4.3602 [4.3581; 4.3623] 0.6 1.5 HIC
## PUERTO RICO 9.6518 [9.6464; 9.6572] 0.5 1.5 HIC
## ROMANIA 0.8871 [0.8865; 0.8878] 0.3 1.5 UMIC
## RUSSIA 0.7291 [0.7288; 0.7293] 1.5 1.5 UMIC
## SAUDI ARABIA 1.0581 [1.0575; 1.0587] 0.4 1.5 HIC
## SERBIA 0.2477 [0.2471; 0.2482] 0.0 1.5 UMIC
## SLOVAKIA 3.4486 [3.4460; 3.4511] 0.3 1.5 HIC
## SLOVENIA 2.6081 [2.6045; 2.6118] 0.1 1.5 HIC
## SOUTH AFRICA 0.2529 [0.2527; 0.2532] 0.2 1.5 UMIC
## SOUTH KOREA 1.8725 [1.8719; 1.8731] 1.3 1.5 HIC
## SPAIN 6.0784 [6.0772; 6.0795] 4.0 1.5 HIC
## SWEDEN 5.1810 [5.1786; 5.1834] 0.7 1.5 HIC
## SWITZERLAND 3.1810 [3.1789; 3.1830] 0.4 1.5 HIC
## TAIWAN 0.2939 [0.2935; 0.2942] 0.1 1.5 HIC
## THAILAND 0.5597 [0.5594; 0.5600] 0.5 1.5 UMIC
## TUNISIA 0.4665 [0.4658; 0.4671] 0.1 1.5 LMIC
## TÜRKIYE 3.5727 [3.5720; 3.5734] 3.8 1.5 UMIC
## UNITED ARAB EMIRATES 0.5663 [0.5655; 0.5671] 0.1 1.5 HIC
## UNITED KINGDOM 8.0419 [8.0408; 8.0431] 7.3 1.5 HIC
## UNITED STATES 9.5958 [9.5952; 9.5963] 42.5 1.5 HIC
## URUGUAY 1.1789 [1.1770; 1.1809] 0.1 1.5 HIC
## VENEZUELA 1.2948 [1.2941; 1.2954] 0.5 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.4758 [4.4756; 4.4760] 76511.88 0
## Random effects model 0.9846 [0.7279; 1.3317] -0.10 0.9195
##
## Quantifying heterogeneity:
## tau^2 = 1.5434 [1.1932; 3.2855]; tau = 1.2423 [1.0923; 1.8126]
## I^2 = 100.0%; H = 7085.04
##
## Test of heterogeneity:
## Q d.f. p-value
## 3212654700.07 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 6.5817 [6.5814; 6.5820] 658864645.86 100.0%
## income = UMIC 21 1.0481 [1.0479; 1.0482] 392771573.62 100.0%
## income = LMIC 6 0.3000 [0.2999; 0.3000] 16126667.16 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2144891813.43 2 0
## Within groups 1067762886.65 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.3940 [1.9480; 2.9420] 0.4203 0.6483
## income = UMIC 21 0.3008 [0.1647; 0.5494] 1.9840 1.4086
## income = LMIC 6 0.2248 [0.1526; 0.3312] 0.2344 0.4842
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 134.97 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.8777 [ 0.8772; 0.8782] 0.4 1.5 UMIC
## ARGENTINA 1.0803 [ 1.0798; 1.0808] 0.6 1.5 UMIC
## AUSTRALIA 6.1076 [ 6.1060; 6.1093] 1.8 1.5 HIC
## AUSTRIA 4.5047 [ 4.5024; 4.5071] 0.5 1.5 HIC
## BELARUS 0.0451 [ 0.0449; 0.0453] 0.0 1.5 UMIC
## BELGIUM 3.1127 [ 3.1110; 3.1144] 0.4 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1600 [ 0.1593; 0.1607] 0.0 1.5 UMIC
## BRAZIL 0.2972 [ 0.2971; 0.2973] 0.8 1.5 UMIC
## BULGARIA 0.5914 [ 0.5905; 0.5924] 0.1 1.5 UMIC
## CANADA 8.1048 [ 8.1032; 8.1063] 3.6 1.5 HIC
## CHILE 0.5859 [ 0.5853; 0.5865] 0.1 1.5 HIC
## CHINA 0.0183 [ 0.0183; 0.0184] 0.3 1.5 UMIC
## COLOMBIA 0.1232 [ 0.1230; 0.1233] 0.1 1.5 UMIC
## CROATIA 0.6205 [ 0.6193; 0.6218] 0.0 1.5 HIC
## CZECH REPUBLIC 3.2558 [ 3.2540; 3.2576] 0.4 1.5 HIC
## ECUADOR 0.4213 [ 0.4207; 0.4218] 0.1 1.5 UMIC
## EGYPT 0.7263 [ 0.7261; 0.7266] 0.8 1.5 LMIC
## ESTONIA 1.0802 [ 1.0772; 1.0831] 0.0 1.5 HIC
## FINLAND 6.5386 [ 6.5351; 6.5422] 0.4 1.5 HIC
## FRANCE 5.2787 [ 5.2778; 5.2797] 4.2 1.5 HIC
## GERMANY 4.7110 [ 4.7102; 4.7117] 4.8 1.5 HIC
## GREECE 3.2833 [ 3.2815; 3.2851] 0.4 1.5 HIC
## HUNGARY 1.7894 [ 1.7880; 1.7908] 0.2 1.5 HIC
## INDIA 0.3310 [ 0.3309; 0.3310] 5.4 1.5 LMIC
## IRELAND 7.5993 [ 7.5952; 7.6035] 0.4 1.5 HIC
## ITALY 2.4579 [ 2.4572; 2.4585] 1.9 1.5 HIC
## JAPAN 2.9447 [ 2.9442; 2.9452] 4.7 1.5 HIC
## JORDAN 0.5784 [ 0.5776; 0.5793] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0671 [ 0.0669; 0.0673] 0.0 1.5 UMIC
## KUWAIT 0.6781 [ 0.6767; 0.6794] 0.0 1.5 HIC
## LATVIA 1.3962 [ 1.3935; 1.3989] 0.0 1.5 HIC
## LEBANON 0.9179 [ 0.9167; 0.9192] 0.1 1.5 UMIC
## LITHUANIA 1.2046 [ 1.2026; 1.2067] 0.0 1.5 HIC
## LUXEMBOURG 4.2040 [ 4.1951; 4.2129] 0.0 1.5 HIC
## MEXICO 0.2951 [ 0.2949; 0.2953] 0.4 1.5 UMIC
## MOROCCO 0.0859 [ 0.0858; 0.0861] 0.0 1.5 LMIC
## NETHERLANDS 3.3712 [ 3.3698; 3.3727] 0.7 1.5 HIC
## NEW ZEALAND 2.5248 [ 2.5224; 2.5272] 0.1 1.5 HIC
## NORWAY 5.0716 [ 5.0683; 5.0748] 0.3 1.5 HIC
## PAKISTAN 0.2485 [ 0.2483; 0.2486] 0.6 1.5 LMIC
## PERU 0.1205 [ 0.1203; 0.1207] 0.0 1.5 UMIC
## PHILIPPINES 0.0950 [ 0.0949; 0.0951] 0.1 1.5 LMIC
## POLAND 0.3720 [ 0.3717; 0.3723] 0.2 1.5 HIC
## PORTUGAL 4.8798 [ 4.8776; 4.8821] 0.6 1.5 HIC
## PUERTO RICO 11.3900 [11.3841; 11.3959] 0.5 1.5 HIC
## ROMANIA 1.0166 [ 1.0159; 1.0174] 0.3 1.5 UMIC
## RUSSIA 0.9373 [ 0.9370; 0.9376] 1.7 1.5 UMIC
## SAUDI ARABIA 1.3930 [ 1.3923; 1.3936] 0.5 1.5 HIC
## SERBIA 0.3969 [ 0.3962; 0.3976] 0.0 1.5 UMIC
## SLOVAKIA 3.7363 [ 3.7336; 3.7390] 0.3 1.5 HIC
## SLOVENIA 2.9582 [ 2.9543; 2.9621] 0.1 1.5 HIC
## SOUTH AFRICA 0.2727 [ 0.2724; 0.2729] 0.2 1.5 UMIC
## SOUTH KOREA 2.0337 [ 2.0331; 2.0344] 1.3 1.5 HIC
## SPAIN 6.3670 [ 6.3658; 6.3682] 3.7 1.5 HIC
## SWEDEN 5.6087 [ 5.6063; 5.6112] 0.7 1.5 HIC
## SWITZERLAND 3.4305 [ 3.4284; 3.4326] 0.4 1.5 HIC
## TAIWAN 0.3606 [ 0.3602; 0.3610] 0.1 1.5 HIC
## THAILAND 0.6334 [ 0.6331; 0.6337] 0.5 1.5 UMIC
## TUNISIA 0.5206 [ 0.5199; 0.5213] 0.1 1.5 LMIC
## TÜRKIYE 4.0914 [ 4.0907; 4.0922] 4.0 1.5 UMIC
## UNITED ARAB EMIRATES 0.8103 [ 0.8094; 0.8113] 0.1 1.5 HIC
## UNITED KINGDOM 9.5420 [ 9.5407; 9.5432] 7.8 1.5 HIC
## UNITED STATES 10.2777 [10.2771; 10.2783] 41.0 1.5 HIC
## URUGUAY 1.3733 [ 1.3712; 1.3753] 0.1 1.5 HIC
## VENEZUELA 1.5927 [ 1.5919; 1.5934] 0.6 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.8238 [4.8237; 4.8240] 85000.72 0
## Random effects model 1.1390 [0.8457; 1.5340] 0.86 0.3915
##
## Quantifying heterogeneity:
## tau^2 = 1.4996 [1.1600; 3.1687]; tau = 1.2246 [1.0770; 1.7801]
## I^2 = 100.0%; H = 7444.25
##
## Test of heterogeneity:
## Q d.f. p-value
## 3546674633.79 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 7.1532 [7.1529; 7.1534] 697272399.00 100.0%
## income = UMIC 21 1.2164 [1.2162; 1.2165] 460494084.81 100.0%
## income = LMIC 6 0.3457 [0.3457; 0.3458] 23703235.57 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2365204914.41 2 0
## Within groups 1181469719.38 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.7143 [2.2225; 3.3148] 0.3952 0.6287
## income = UMIC 21 0.3643 [0.2013; 0.6594] 1.9249 1.3874
## income = LMIC 6 0.2517 [0.1632; 0.3881] 0.2929 0.5412
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 120.75 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.0435 [ 1.0430; 1.0440] 0.5 1.5 UMIC
## ARGENTINA 1.2359 [ 1.2353; 1.2364] 0.6 1.5 UMIC
## AUSTRALIA 7.5188 [ 7.5170; 7.5207] 2.0 1.5 HIC
## AUSTRIA 4.6332 [ 4.6308; 4.6355] 0.4 1.5 HIC
## BELARUS 0.0609 [ 0.0607; 0.0612] 0.0 1.5 UMIC
## BELGIUM 3.4623 [ 3.4605; 3.4641] 0.4 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.2186 [ 0.2178; 0.2194] 0.0 1.5 UMIC
## BRAZIL 0.3671 [ 0.3669; 0.3672] 0.8 1.5 UMIC
## BULGARIA 0.6868 [ 0.6858; 0.6878] 0.1 1.5 UMIC
## CANADA 8.9837 [ 8.9821; 8.9854] 3.6 1.5 HIC
## CHILE 0.6971 [ 0.6964; 0.6977] 0.1 1.5 HIC
## CHINA 0.0236 [ 0.0236; 0.0236] 0.4 1.5 UMIC
## COLOMBIA 0.1650 [ 0.1648; 0.1652] 0.1 1.5 UMIC
## CROATIA 0.7200 [ 0.7187; 0.7214] 0.0 1.5 HIC
## CZECH REPUBLIC 3.8896 [ 3.8876; 3.8915] 0.5 1.5 HIC
## ECUADOR 0.4820 [ 0.4815; 0.4826] 0.1 1.5 UMIC
## EGYPT 0.9495 [ 0.9492; 0.9498] 1.0 1.5 LMIC
## ESTONIA 1.3609 [ 1.3576; 1.3642] 0.0 1.5 HIC
## FINLAND 6.6345 [ 6.6310; 6.6381] 0.4 1.5 HIC
## FRANCE 5.5188 [ 5.5179; 5.5198] 4.0 1.5 HIC
## GERMANY 4.9624 [ 4.9616; 4.9632] 4.5 1.5 HIC
## GREECE 3.3903 [ 3.3884; 3.3921] 0.4 1.5 HIC
## HUNGARY 1.7720 [ 1.7706; 1.7734] 0.2 1.5 HIC
## INDIA 0.3751 [ 0.3750; 0.3751] 5.5 1.5 LMIC
## IRELAND 9.3143 [ 9.3098; 9.3189] 0.5 1.5 HIC
## ITALY 2.5593 [ 2.5587; 2.5600] 1.7 1.5 HIC
## JAPAN 3.2023 [ 3.2018; 3.2029] 4.6 1.5 HIC
## JORDAN 0.5796 [ 0.5788; 0.5804] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0902 [ 0.0899; 0.0904] 0.0 1.5 UMIC
## KUWAIT 0.7833 [ 0.7818; 0.7847] 0.0 1.5 HIC
## LATVIA 1.6802 [ 1.6772; 1.6832] 0.0 1.5 HIC
## LEBANON 1.0485 [ 1.0472; 1.0498] 0.1 1.5 UMIC
## LITHUANIA 1.2834 [ 1.2812; 1.2855] 0.0 1.5 HIC
## LUXEMBOURG 4.1852 [ 4.1764; 4.1940] 0.0 1.5 HIC
## MEXICO 0.3186 [ 0.3184; 0.3187] 0.4 1.5 UMIC
## MOROCCO 0.1033 [ 0.1032; 0.1035] 0.0 1.5 LMIC
## NETHERLANDS 3.5567 [ 3.5552; 3.5582] 0.7 1.5 HIC
## NEW ZEALAND 2.9494 [ 2.9468; 2.9520] 0.2 1.5 HIC
## NORWAY 5.6805 [ 5.6771; 5.6839] 0.3 1.5 HIC
## PAKISTAN 0.2817 [ 0.2816; 0.2818] 0.6 1.5 LMIC
## PERU 0.1993 [ 0.1991; 0.1996] 0.1 1.5 UMIC
## PHILIPPINES 0.1056 [ 0.1055; 0.1057] 0.1 1.5 LMIC
## POLAND 0.4692 [ 0.4688; 0.4696] 0.2 1.5 HIC
## PORTUGAL 5.0680 [ 5.0657; 5.0703] 0.6 1.5 HIC
## PUERTO RICO 12.9825 [12.9762; 12.9889] 0.5 1.5 HIC
## ROMANIA 1.1237 [ 1.1229; 1.1244] 0.3 1.5 UMIC
## RUSSIA 0.8413 [ 0.8411; 0.8416] 1.4 1.5 UMIC
## SAUDI ARABIA 1.2320 [ 1.2313; 1.2326] 0.4 1.5 HIC
## SERBIA 0.7818 [ 0.7808; 0.7827] 0.1 1.5 UMIC
## SLOVAKIA 4.1169 [ 4.1141; 4.1198] 0.3 1.5 HIC
## SLOVENIA 3.2621 [ 3.2581; 3.2662] 0.1 1.5 HIC
## SOUTH AFRICA 0.2909 [ 0.2907; 0.2912] 0.2 1.5 UMIC
## SOUTH KOREA 2.1810 [ 2.1803; 2.1817] 1.2 1.5 HIC
## SPAIN 6.4617 [ 6.4605; 6.4629] 3.4 1.5 HIC
## SWEDEN 6.0197 [ 6.0172; 6.0223] 0.7 1.5 HIC
## SWITZERLAND 3.7244 [ 3.7222; 3.7265] 0.3 1.5 HIC
## TAIWAN 0.4099 [ 0.4094; 0.4103] 0.1 1.5 HIC
## THAILAND 0.6688 [ 0.6685; 0.6691] 0.5 1.5 UMIC
## TUNISIA 0.5660 [ 0.5653; 0.5668] 0.1 1.5 LMIC
## TÜRKIYE 4.6908 [ 4.6900; 4.6916] 4.1 1.5 UMIC
## UNITED ARAB EMIRATES 1.0947 [ 1.0936; 1.0958] 0.1 1.5 HIC
## UNITED KINGDOM 11.0046 [11.0033; 11.0059] 8.1 1.5 HIC
## UNITED STATES 11.6703 [11.6696; 11.6709] 41.8 1.5 HIC
## URUGUAY 1.4897 [ 1.4876; 1.4919] 0.1 1.5 HIC
## VENEZUELA 1.0882 [ 1.0875; 1.0888] 0.4 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.3991 [5.3989; 5.3993] 96410.91 0
## Random effects model 1.2931 [0.9533; 1.7539] 1.65 0.0984
##
## Quantifying heterogeneity:
## tau^2 = 1.5725 [1.1645; 3.1814]; tau = 1.2540 [1.0791; 1.7836]
## I^2 = 100.0%; H = 8034.92
##
## Test of heterogeneity:
## Q d.f. p-value
## 4131832821.55 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 8.0696 [8.0693; 8.0699] 840311700.07 100.0%
## income = UMIC 21 1.3092 [1.3090; 1.3093] 551256836.58 100.0%
## income = LMIC 6 0.4046 [0.4046; 0.4047] 36855212.33 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2703409072.58 2 0
## Within groups 1428423748.98 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 3.0028 [2.4366; 3.7006] 0.4318 0.6571
## income = UMIC 21 0.4307 [0.2310; 0.8030] 2.1215 1.4566
## income = LMIC 6 0.2920 [0.1792; 0.4757] 0.3721 0.6100
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 96.61 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.4666 [ 1.4659; 1.4672] 0.6 1.5 UMIC
## ARGENTINA 1.2701 [ 1.2696; 1.2707] 0.6 1.5 UMIC
## AUSTRALIA 8.6392 [ 8.6372; 8.6411] 2.1 1.5 HIC
## AUSTRIA 5.5558 [ 5.5533; 5.5584] 0.5 1.5 HIC
## BELARUS 0.0745 [ 0.0742; 0.0748] 0.0 1.5 UMIC
## BELGIUM 4.6625 [ 4.6604; 4.6645] 0.5 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.3015 [ 0.3006; 0.3025] 0.0 1.5 UMIC
## BRAZIL 0.4401 [ 0.4399; 0.4402] 0.9 1.5 UMIC
## BULGARIA 0.9792 [ 0.9780; 0.9804] 0.1 1.5 UMIC
## CANADA 9.9954 [ 9.9937; 9.9971] 3.7 1.5 HIC
## CHILE 0.7907 [ 0.7900; 0.7914] 0.1 1.5 HIC
## CHINA 0.0286 [ 0.0286; 0.0286] 0.4 1.5 UMIC
## COLOMBIA 0.1667 [ 0.1665; 0.1669] 0.1 1.5 UMIC
## CROATIA 1.0827 [ 1.0810; 1.0843] 0.0 1.5 HIC
## CZECH REPUBLIC 4.9366 [ 4.9344; 4.9388] 0.5 1.5 HIC
## ECUADOR 0.4980 [ 0.4975; 0.4986] 0.1 1.5 UMIC
## EGYPT 1.3411 [ 1.3407; 1.3414] 1.3 1.5 LMIC
## ESTONIA 1.7473 [ 1.7435; 1.7510] 0.0 1.5 HIC
## FINLAND 7.1434 [ 7.1397; 7.1470] 0.4 1.5 HIC
## FRANCE 5.6502 [ 5.6492; 5.6511] 3.7 1.5 HIC
## GERMANY 5.2710 [ 5.2702; 5.2718] 4.4 1.5 HIC
## GREECE 3.9338 [ 3.9318; 3.9358] 0.4 1.5 HIC
## HUNGARY 1.9009 [ 1.8995; 1.9024] 0.2 1.5 HIC
## INDIA 0.4165 [ 0.4165; 0.4166] 5.6 1.5 LMIC
## IRELAND 10.0262 [10.0215; 10.0310] 0.5 1.5 HIC
## ITALY 2.6128 [ 2.6122; 2.6135] 1.6 1.5 HIC
## JAPAN 3.5135 [ 3.5129; 3.5140] 4.6 1.5 HIC
## JORDAN 0.8999 [ 0.8989; 0.9009] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0727 [ 0.0725; 0.0729] 0.0 1.5 UMIC
## KUWAIT 1.3133 [ 1.3114; 1.3152] 0.1 1.5 HIC
## LATVIA 1.8527 [ 1.8495; 1.8558] 0.0 1.5 HIC
## LEBANON 1.3224 [ 1.3210; 1.3239] 0.1 1.5 UMIC
## LITHUANIA 1.6490 [ 1.6465; 1.6514] 0.0 1.5 HIC
## LUXEMBOURG 4.3268 [ 4.3179; 4.3357] 0.0 1.5 HIC
## MEXICO 0.3396 [ 0.3394; 0.3397] 0.4 1.5 UMIC
## MOROCCO 0.1130 [ 0.1129; 0.1132] 0.0 1.5 LMIC
## NETHERLANDS 3.7671 [ 3.7656; 3.7687] 0.7 1.5 HIC
## NEW ZEALAND 3.4626 [ 3.4598; 3.4654] 0.2 1.5 HIC
## NORWAY 5.8838 [ 5.8804; 5.8872] 0.3 1.5 HIC
## PAKISTAN 0.3136 [ 0.3135; 0.3137] 0.6 1.5 LMIC
## PERU 0.2131 [ 0.2128; 0.2134] 0.1 1.5 UMIC
## PHILIPPINES 0.1206 [ 0.1205; 0.1207] 0.1 1.5 LMIC
## POLAND 0.7797 [ 0.7792; 0.7801] 0.3 1.5 HIC
## PORTUGAL 4.9211 [ 4.9189; 4.9233] 0.5 1.5 HIC
## PUERTO RICO 14.3848 [14.3780; 14.3916] 0.5 1.5 HIC
## ROMANIA 1.3004 [ 1.2996; 1.3013] 0.3 1.5 UMIC
## RUSSIA 0.2778 [ 0.2776; 0.2779] 0.4 1.5 UMIC
## SAUDI ARABIA 1.3045 [ 1.3039; 1.3052] 0.4 1.5 HIC
## SERBIA 1.1215 [ 1.1203; 1.1226] 0.1 1.5 UMIC
## SLOVAKIA 5.0693 [ 5.0662; 5.0724] 0.3 1.5 HIC
## SLOVENIA 3.6342 [ 3.6299; 3.6385] 0.1 1.5 HIC
## SOUTH AFRICA 0.3238 [ 0.3236; 0.3241] 0.2 1.5 UMIC
## SOUTH KOREA 2.4374 [ 2.4367; 2.4381] 1.3 1.5 HIC
## SPAIN 6.8250 [ 6.8237; 6.8262] 3.2 1.5 HIC
## SWEDEN 6.6761 [ 6.6734; 6.6787] 0.7 1.5 HIC
## SWITZERLAND 3.9619 [ 3.9597; 3.9641] 0.3 1.5 HIC
## TAIWAN 0.4897 [ 0.4893; 0.4902] 0.1 1.5 HIC
## THAILAND 0.7037 [ 0.7034; 0.7040] 0.5 1.5 UMIC
## TUNISIA 0.6241 [ 0.6234; 0.6249] 0.1 1.5 LMIC
## TÜRKIYE 5.0237 [ 5.0229; 5.0246] 4.1 1.5 UMIC
## UNITED ARAB EMIRATES 0.7763 [ 0.7753; 0.7772] 0.1 1.5 HIC
## UNITED KINGDOM 12.5535 [12.5521; 12.5549] 8.5 1.5 HIC
## UNITED STATES 12.8246 [12.8239; 12.8252] 42.1 1.5 HIC
## URUGUAY 1.5550 [ 1.5528; 1.5572] 0.1 1.5 HIC
## VENEZUELA 0.6046 [ 0.6041; 0.6051] 0.2 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.9755 [5.9753; 5.9756] 107168.22 0
## Random effects model 1.4404 [1.0592; 1.9588] 2.33 0.0200
##
## Quantifying heterogeneity:
## tau^2 = 1.5987 [1.1852; 3.2346]; tau = 1.2644 [1.0887; 1.7985]
## I^2 = 100.0%; H = 8481.40
##
## Test of heterogeneity:
## Q d.f. p-value
## 4603783703.11 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 8.8845 [8.8842; 8.8848] 954293574.67 100.0%
## income = UMIC 21 1.3865 [1.3863; 1.3866] 637318598.48 100.0%
## income = LMIC 6 0.4822 [0.4822; 0.4823] 69030341.86 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2943141188.10 2 0
## Within groups 1660642515.01 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 3.4117 [2.7602; 4.2169] 0.4441 0.6664
## income = UMIC 21 0.4579 [0.2325; 0.9019] 2.5121 1.5850
## income = LMIC 6 0.3380 [0.1864; 0.6128] 0.5531 0.7437
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 74.97 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 3.0485 [ 3.0476; 3.0494] 1.2 1.5 UMIC
## ARGENTINA 1.3337 [ 1.3331; 1.3342] 0.5 1.5 UMIC
## AUSTRALIA 9.3000 [ 9.2980; 9.3020] 2.1 1.5 HIC
## AUSTRIA 5.7414 [ 5.7388; 5.7440] 0.5 1.5 HIC
## BELARUS 0.1234 [ 0.1230; 0.1238] 0.0 1.5 UMIC
## BELGIUM 5.2455 [ 5.2433; 5.2477] 0.5 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.3756 [ 0.3746; 0.3767] 0.0 1.5 UMIC
## BRAZIL 0.5157 [ 0.5155; 0.5158] 1.0 1.5 UMIC
## BULGARIA 1.2179 [ 1.2166; 1.2192] 0.1 1.5 UMIC
## CANADA 10.7934 [10.7916; 10.7951] 3.6 1.5 HIC
## CHILE 0.8795 [ 0.8788; 0.8802] 0.1 1.5 HIC
## CHINA 0.0358 [ 0.0358; 0.0358] 0.5 1.5 UMIC
## COLOMBIA 0.1728 [ 0.1727; 0.1730] 0.1 1.5 UMIC
## CROATIA 1.2346 [ 1.2328; 1.2364] 0.0 1.5 HIC
## CZECH REPUBLIC 5.6135 [ 5.6112; 5.6159] 0.5 1.5 HIC
## ECUADOR 0.5272 [ 0.5266; 0.5277] 0.1 1.5 UMIC
## EGYPT 2.0563 [ 2.0558; 2.0568] 1.8 1.5 LMIC
## ESTONIA 2.2727 [ 2.2684; 2.2769] 0.0 1.5 HIC
## FINLAND 7.7485 [ 7.7446; 7.7523] 0.4 1.5 HIC
## FRANCE 5.7629 [ 5.7619; 5.7639] 3.4 1.5 HIC
## GERMANY 5.3977 [ 5.3969; 5.3985] 4.1 1.5 HIC
## GREECE 4.2748 [ 4.2727; 4.2768] 0.4 1.5 HIC
## HUNGARY 2.0198 [ 2.0184; 2.0213] 0.2 1.5 HIC
## INDIA 0.4575 [ 0.4575; 0.4576] 5.6 1.5 LMIC
## IRELAND 10.1613 [10.1566; 10.1661] 0.4 1.5 HIC
## ITALY 2.8330 [ 2.8323; 2.8337] 1.6 1.5 HIC
## JAPAN 3.8741 [ 3.8736; 3.8747] 4.5 1.5 HIC
## JORDAN 1.1300 [ 1.1289; 1.1311] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0892 [ 0.0890; 0.0894] 0.0 1.5 UMIC
## KUWAIT 2.6181 [ 2.6155; 2.6207] 0.1 1.5 HIC
## LATVIA 2.1990 [ 2.1955; 2.2024] 0.0 1.5 HIC
## LEBANON 1.4841 [ 1.4826; 1.4856] 0.1 1.5 UMIC
## LITHUANIA 1.7040 [ 1.7015; 1.7065] 0.0 1.5 HIC
## LUXEMBOURG 4.4402 [ 4.4314; 4.4491] 0.0 1.5 HIC
## MEXICO 0.3813 [ 0.3811; 0.3815] 0.4 1.5 UMIC
## MOROCCO 0.1463 [ 0.1461; 0.1465] 0.0 1.5 LMIC
## NETHERLANDS 3.8860 [ 3.8844; 3.8875] 0.6 1.5 HIC
## NEW ZEALAND 3.8784 [ 3.8754; 3.8813] 0.2 1.5 HIC
## NORWAY 6.3251 [ 6.3215; 6.3286] 0.3 1.5 HIC
## PAKISTAN 0.3403 [ 0.3402; 0.3405] 0.6 1.5 LMIC
## PERU 0.2237 [ 0.2234; 0.2239] 0.1 1.5 UMIC
## PHILIPPINES 0.1306 [ 0.1305; 0.1307] 0.1 1.5 LMIC
## POLAND 1.2022 [ 1.2016; 1.2028] 0.4 1.5 HIC
## PORTUGAL 4.9841 [ 4.9818; 4.9863] 0.5 1.5 HIC
## PUERTO RICO 16.0002 [15.9929; 16.0075] 0.5 1.5 HIC
## ROMANIA 1.5396 [ 1.5387; 1.5405] 0.3 1.5 UMIC
## RUSSIA 0.3809 [ 0.3807; 0.3810] 0.5 1.5 UMIC
## SAUDI ARABIA 1.8736 [ 1.8729; 1.8744] 0.6 1.5 HIC
## SERBIA 1.4789 [ 1.4775; 1.4802] 0.1 1.5 UMIC
## SLOVAKIA 4.7357 [ 4.7327; 4.7387] 0.2 1.5 HIC
## SLOVENIA 3.8317 [ 3.8273; 3.8361] 0.1 1.5 HIC
## SOUTH AFRICA 0.3350 [ 0.3347; 0.3352] 0.2 1.5 UMIC
## SOUTH KOREA 2.7612 [ 2.7604; 2.7619] 1.3 1.5 HIC
## SPAIN 7.1036 [ 7.1024; 7.1049] 3.0 1.5 HIC
## SWEDEN 7.1681 [ 7.1654; 7.1709] 0.6 1.5 HIC
## SWITZERLAND 4.0655 [ 4.0633; 4.0678] 0.3 1.5 HIC
## TAIWAN 0.5447 [ 0.5442; 0.5452] 0.1 1.5 HIC
## THAILAND 0.8093 [ 0.8090; 0.8097] 0.5 1.5 UMIC
## TUNISIA 0.8396 [ 0.8388; 0.8405] 0.1 1.5 LMIC
## TÜRKIYE 5.8508 [ 5.8499; 5.8517] 4.3 1.5 UMIC
## UNITED ARAB EMIRATES 0.6780 [ 0.6771; 0.6789] 0.1 1.5 HIC
## UNITED KINGDOM 13.5562 [13.5548; 13.5577] 8.3 1.5 HIC
## UNITED STATES 14.0011 [14.0005; 14.0018] 41.7 1.5 HIC
## URUGUAY 1.7113 [ 1.7090; 1.7135] 0.1 1.5 HIC
## VENEZUELA 0.4621 [ 0.4617; 0.4625] 0.1 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.4047 [6.4045; 6.4049] 117306.87 0
## Random effects model 1.6515 [1.2170; 2.2411] 3.22 0.0013
##
## Quantifying heterogeneity:
## tau^2 = 1.5771 [1.1376; 3.0811]; tau = 1.2558 [1.0666; 1.7553]
## I^2 = 100.0%; H = 8899.55
##
## Test of heterogeneity:
## Q d.f. p-value
## 5068932381.55 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 9.6015 [9.6012; 9.6018] 1061864216.10 100.0%
## income = UMIC 21 1.7155 [1.7153; 1.7157] 778240991.61 100.0%
## income = LMIC 6 0.6087 [0.6086; 0.6087] 150422067.40 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 3078405106.44 2 0
## Within groups 1990527275.10 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 3.7824 [3.0515; 4.6884] 0.4561 0.6754
## income = UMIC 21 0.5469 [0.2798; 1.0688] 2.4541 1.5666
## income = LMIC 6 0.4154 [0.1930; 0.8940] 0.9175 0.9579
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 54.15 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 2.9435 [ 2.9427; 2.9444] 1.1 1.5 UMIC
## ARGENTINA 1.3902 [ 1.3896; 1.3907] 0.5 1.5 UMIC
## AUSTRALIA 9.4384 [ 9.4364; 9.4404] 2.0 1.5 HIC
## AUSTRIA 6.0504 [ 6.0478; 6.0531] 0.5 1.5 HIC
## BELARUS 0.1868 [ 0.1863; 0.1872] 0.0 1.5 UMIC
## BELGIUM 5.6263 [ 5.6240; 5.6286] 0.6 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.5333 [ 0.5320; 0.5346] 0.0 1.5 UMIC
## BRAZIL 0.6318 [ 0.6316; 0.6320] 1.1 1.5 UMIC
## BULGARIA 1.3771 [ 1.3757; 1.3785] 0.1 1.5 UMIC
## CANADA 10.8953 [10.8936; 10.8971] 3.5 1.5 HIC
## CHILE 0.9280 [ 0.9273; 0.9288] 0.1 1.5 HIC
## CHINA 0.0438 [ 0.0438; 0.0438] 0.5 1.5 UMIC
## COLOMBIA 0.1955 [ 0.1953; 0.1957] 0.1 1.5 UMIC
## CROATIA 1.3816 [ 1.3797; 1.3834] 0.0 1.5 HIC
## CZECH REPUBLIC 6.0546 [ 6.0522; 6.0571] 0.6 1.5 HIC
## ECUADOR 0.5475 [ 0.5469; 0.5481] 0.1 1.5 UMIC
## EGYPT 3.9544 [ 3.9538; 3.9551] 3.3 1.5 LMIC
## ESTONIA 3.0000 [ 2.9952; 3.0049] 0.0 1.5 HIC
## FINLAND 8.2826 [ 8.2786; 8.2865] 0.4 1.5 HIC
## FRANCE 6.0179 [ 6.0169; 6.0188] 3.4 1.5 HIC
## GERMANY 5.6636 [ 5.6628; 5.6645] 4.0 1.5 HIC
## GREECE 4.6340 [ 4.6318; 4.6361] 0.4 1.5 HIC
## HUNGARY 2.1507 [ 2.1492; 2.1522] 0.2 1.5 HIC
## INDIA 0.5129 [ 0.5129; 0.5130] 6.0 1.5 LMIC
## IRELAND 8.5541 [ 8.5498; 8.5584] 0.4 1.5 HIC
## ITALY 2.8818 [ 2.8811; 2.8825] 1.5 1.5 HIC
## JAPAN 4.1321 [ 4.1315; 4.1327] 4.5 1.5 HIC
## JORDAN 0.6513 [ 0.6504; 0.6521] 0.1 1.5 UMIC
## KAZAKHSTAN 0.1047 [ 0.1044; 0.1049] 0.0 1.5 UMIC
## KUWAIT 3.2250 [ 3.2222; 3.2279] 0.1 1.5 HIC
## LATVIA 2.6394 [ 2.6357; 2.6432] 0.0 1.5 HIC
## LEBANON 1.5687 [ 1.5672; 1.5703] 0.1 1.5 UMIC
## LITHUANIA 2.0118 [ 2.0091; 2.0146] 0.0 1.5 HIC
## LUXEMBOURG 4.4238 [ 4.4151; 4.4326] 0.0 1.5 HIC
## MEXICO 0.4126 [ 0.4124; 0.4128] 0.4 1.5 UMIC
## MOROCCO 0.2011 [ 0.2009; 0.2014] 0.1 1.5 LMIC
## NETHERLANDS 4.0538 [ 4.0522; 4.0554] 0.6 1.5 HIC
## NEW ZEALAND 5.0261 [ 5.0228; 5.0295] 0.2 1.5 HIC
## NORWAY 6.6320 [ 6.6284; 6.6356] 0.3 1.5 HIC
## PAKISTAN 0.3993 [ 0.3992; 0.3995] 0.7 1.5 LMIC
## PERU 0.2562 [ 0.2559; 0.2565] 0.1 1.5 UMIC
## PHILIPPINES 0.1574 [ 0.1573; 0.1576] 0.1 1.5 LMIC
## POLAND 1.6797 [ 1.6790; 1.6803] 0.5 1.5 HIC
## PORTUGAL 5.1522 [ 5.1499; 5.1545] 0.5 1.5 HIC
## PUERTO RICO 15.1233 [15.1160; 15.1305] 0.4 1.5 HIC
## ROMANIA 1.6365 [ 1.6356; 1.6375] 0.3 1.5 UMIC
## RUSSIA 0.5050 [ 0.5048; 0.5052] 0.6 1.5 UMIC
## SAUDI ARABIA 1.1357 [ 1.1351; 1.1363] 0.3 1.5 HIC
## SERBIA 1.9559 [ 1.9543; 1.9574] 0.1 1.5 UMIC
## SLOVAKIA 5.1626 [ 5.1594; 5.1657] 0.2 1.5 HIC
## SLOVENIA 3.9999 [ 3.9954; 4.0044] 0.1 1.5 HIC
## SOUTH AFRICA 0.3738 [ 0.3736; 0.3741] 0.2 1.5 UMIC
## SOUTH KOREA 3.0995 [ 3.0987; 3.1003] 1.4 1.5 HIC
## SPAIN 7.3282 [ 7.3269; 7.3295] 2.9 1.5 HIC
## SWEDEN 7.6667 [ 7.6638; 7.6695] 0.7 1.5 HIC
## SWITZERLAND 4.2259 [ 4.2237; 4.2282] 0.3 1.5 HIC
## TAIWAN 0.6414 [ 0.6408; 0.6419] 0.1 1.5 HIC
## THAILAND 0.9387 [ 0.9383; 0.9390] 0.6 1.5 UMIC
## TUNISIA 2.0328 [ 2.0314; 2.0342] 0.2 1.5 LMIC
## TÜRKIYE 6.0983 [ 6.0974; 6.0992] 4.3 1.5 UMIC
## UNITED ARAB EMIRATES 0.6640 [ 0.6631; 0.6648] 0.1 1.5 HIC
## UNITED KINGDOM 13.8879 [13.8864; 13.8894] 8.0 1.5 HIC
## UNITED STATES 14.2539 [14.2532; 14.2546] 40.1 1.5 HIC
## URUGUAY 1.5402 [ 1.5380; 1.5424] 0.0 1.5 HIC
## VENEZUELA 0.4182 [ 0.4178; 0.4186] 0.1 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.4210 [6.4208; 6.4212] 121182.01 0
## Random effects model 1.8260 [1.3537; 2.4631] 3.94 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.5159 [1.0781; 2.8964]; tau = 1.2312 [1.0383; 1.7019]
## I^2 = 100.0%; H = 9069.14
##
## Test of heterogeneity:
## Q d.f. p-value
## 5263957332.51 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 9.8006 [9.8003; 9.8009] 1056439201.52 100.0%
## income = UMIC 21 1.7279 [1.7277; 1.7281] 823324539.57 100.0%
## income = LMIC 6 0.9741 [0.9740; 0.9742] 439513652.09 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2944679939.34 2 0
## Within groups 2319277393.17 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 3.9922 [3.2354; 4.9260] 0.4370 0.6611
## income = UMIC 21 0.6061 [0.3151; 1.1660] 2.3399 1.5297
## income = LMIC 6 0.6112 [0.2121; 1.7611] 1.7491 1.3226
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 38.48 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
##income
gaba_income<-as.data.frame(do.call(rbind, datout))[c(5:10)]
gaba_income$Year<-as.numeric(gaba_income$Year)
out <- unlist(gaba_income)
Year<-out[1:44]
Drug<-out[45:88]
DDDTID<-out[89:220]
lower<-out[221:352]
upper<-out[353:484]
out2<-data.frame(Year,Drug)
out3<-do.call("rbind", replicate(3, out2, simplify = FALSE))
newdata <- out3%>% arrange(Drug, Year)
newdata$DDDTID<-DDDTID
newdata$lower<-lower
newdata$upper<-upper
region_name<-(rep(c("HIC","UMIC","LMIC"),44))
newdata$Income<-region_name
newdata$Drug<-c(rep(("Gabapentin"), 33),
rep(("Gabapentin Enacarbil"), 33),
rep(("Pregabalin"), 33),
rep(("Gabapentinoids"), 33))
gaba_income2<-newdata
gaba_income2$`DDD/TID`<-gaba_income2$DDDTID
gaba_income2$`DDD/TID - lower`<-gaba_income2$lower
gaba_income2$`DDD/TID - upper`<-gaba_income2$upper
gaba_income3<-gaba_income2[c(1,2,6:9)]
library(DT)
datatable(gaba_income3, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
write.csv(gaba_income3,"D:/R/midas gaba/R1/Output_6_Subgroup_AAPC_income_meta.csv")
Gaba available in different countries and regions in 2018
library(dplyr)
library(readxl)
library(tidytext)
#df with year, country, drug name (Y/N)
avail<-subset(cty.ddd.3)
avail.1<-subset(avail, DDD!=0)
avail.2<-table(avail.1$Drug, avail.1$Year)
avail.2<-as.data.frame(avail.2)
names(avail.2)<-c("Drug", "Year", "Number of Countries")
avail.3<-avail.1[c(1,4)]
avail.4<-unique(avail.3)
avail.5<-table(avail.4$Year)
avail.5<-as.data.frame(avail.5)
avail.5$Drug<-"Gabapentinoids"
avail.6<-avail.5[c(1,3,2)]
names(avail.6)<-c("Year", "Drug", "Number of Countries")
avail.year<-rbind(avail.2,avail.6)
avail.year
library(dplyr)
library(readxl)
library(tidytext)
#df with year, country, drug name (Y/N)
avail<-subset(cty.ddd.3, Year==2018)
avail.1<-subset(avail, DDD!=0)
avail.1$Availability<-"No"
avail.1$Availability[avail.1$DDDPTPD>0]<-"Yes"
avail.1$Region <- factor(avail.1$Region, levels =
c("Northern America","Central and South America and the Caribbean",
"Northern Europe", "Eastern Europe","Southern Europe", "Western Europe",
"Australia and New Zealand" ,
"Eastern Asia" , "Central Asia",
"South-eastern Asia","Southern Asia" ,
"Western Asia", "Sub-Saharan Africa",
"Northern Africa"
))
avail.2 <- avail.1 %>%
arrange(Region,Country) %>% # sort your dataframe
mutate(Country = factor(Country, unique(Country))) # reset your factor-column based on that order
# ggplot(avail.2, aes(Drug, Country, fill= Availability)) +
# geom_tile(colour = "black")+
# scale_y_discrete(limits=rev)+
# scale_fill_manual(values=c("darkgreen"),na.value = "white")
# by region
# ggplot(avail.1, aes(Drug, Region, fill= Availability)) +
# geom_tile(colour = "black")+
# scale_y_discrete(limits=rev)+
# scale_fill_manual(values=c("darkgreen"),na.value = "white")
#by income level
library(readxl)
avail.3 <- left_join(avail.2,GDP,by = c("Country") )
avail.3$income <- factor(avail.3$income, levels =
c("HIC","UMIC","LMIC"))
avail.3 <- avail.3 %>%
arrange(income,Region, Country) %>% # sort your dataframe
mutate(Country = factor(Country, unique(Country))) # reset your factor-column based on that order
ggplot(avail.3, aes(Drug, Country, fill= Availability)) +
geom_tile(colour = "black")+
scale_y_discrete(limits=rev)+
scale_fill_manual(values=c("darkgreen"),na.value = "white")+
theme_bw(base_size = 20)+ theme(panel.grid.major = element_blank())
avail.4<-unique(avail.3[c(4,5,8)])
avail.4$Country<-as.character(avail.4$Country)
## Wallis comment: check data source:
library(tidyr)
library(DT)
check_source3<-unique(data.1.nodup[c(1,2)])
check_source3<-pivot_wider(check_source3,names_from = SEC,values_from = SEC)
check_source3$CTY = sub(pattern = "(RETAIL)|(COMBINED)|(COMBINE)|(COMBIN)|(COMBI)|(RET)|(R.MUTUALES)|(HOSPITAL)|(TOTAL SALES)",
replacement = "", x = check_source3$CTY, perl = TRUE)
colnames(check_source3)[1] <- "Country"
check_source3<- subset(check_source3, !Country=="C. AMERICA"&
!Country=="FR. W. AFRICA"&
!Country=="FRENCH WEST AFRICA"&
!Country=="CENTRAL AMERICA")
check_source3$Country<-trimws(check_source3$Country, which = c("right"), whitespace = "[ \t\r\n]")
check_source3[check_source3$Country == "CZECH", "Country"] <- "CZECH REPUBLIC"
check_source3[check_source3$Country == "NETHERLNDS", "Country"] <- "NETHERLANDS"
check_source3[check_source3$Country == "RUSSIAN FED.", "Country"] <- "RUSSIA"
check_source3[check_source3$Country == "TURKEY", "Country"] <- "TÜRKİYE"
check_source3[check_source3$Country == "UAE", "Country"] <- "UNITED ARAB EMIRATES"
check_source3[check_source3$Country == "UK", "Country"] <- "UNITED KINGDOM"
check_source3[check_source3$Country == "USA", "Country"] <- "UNITED STATES"
check_source3[check_source3$Country == "US", "Country"] <- "UNITED STATES"
check_source3[check_source3$Country == "S. AFRICA", "Country"] <- "SOUTH AFRICA"
check_source3[check_source3$Country == "BOSNIA", "Country"] <- "BOSNIA AND HERZEGOVINA"
check_source4 <- left_join(check_source3,avail.4,by = c("Country") )
datatable(check_source4, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
write.csv(check_source4, file = "D:/R/midas gaba/R1/Reference_data_source5.csv")
Subgroup: country income level
Gabapentinoids
library(readxl)
GDP <- read_excel("D:/R/midas gaba/Income.xlsx")
'%ni%' <- Negate('%in%')
GDP$country <- toupper(GDP$country)
rename <- lm.cty_gaba%>%filter(Country %ni% GDP$country)
rename<-unique(rename$Country)
names(GDP)[names(GDP) == 'country'] <- "Country"
GDP[GDP$Country == "KOREA, REP.", "Country"] <- "SOUTH KOREA"
GDP[GDP$Country == "EGYPT, ARAB REP.", "Country"] <- "EGYPT"
GDP[GDP$Country == "RUSSIAN FEDERATION", "Country"] <- "RUSSIA"
GDP[GDP$Country == "SLOVAK REPUBLIC", "Country"] <- "SLOVAKIA"
GDP[GDP$Country == "VENEZUEL", "Country"] <- "VENEZUELA"
GDP[GDP$Country == "TURKEY", "Country"] <- "TÜRKİYE"
lm.cty_gaba$Year<-as.numeric(lm.cty_gaba$Year)
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Gabapentinoids")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
## [[1]]
## lower est.
## Year 0.1630294 0.2013802
## upper numDF denDF
## Year 0.2397311 1 187
## F-value p-value Model
## Year 107.3048 0 UMIC
## AAPC explower
## Year 22.30897 17.70713
## expupper
## Year 27.09074
##
## [[2]]
## lower est.
## Year 0.1169225 0.1326198
## upper numDF denDF
## Year 0.1483172 1 366
## F-value p-value Model
## Year 276.018 0 HIC
## AAPC explower
## Year 14.18158 12.40323
## expupper
## Year 15.98807
##
## [[3]]
## lower est.
## Year 0.1696984 0.2093044
## upper numDF denDF
## Year 0.2489104 1 59
## F-value
## Year 111.8218
## p-value
## Year 0.000000000000003108624
## Model AAPC explower
## Year LMIC 23.28202 18.49474
## expupper
## Year 28.26271
gabapentinoids.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
gabapentinoids.mlm_income$Drug<-"Gabapentinoids"
datatable(as.data.frame(gabapentinoids.mlm_income[,c(8:11,7)]),caption = "Average annual percentage change by income level", options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Gabapentin
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Gabapentin")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
gabapentin.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
gabapentin.mlm_income<-as.data.frame(gabapentin.mlm_income)
gabapentin.mlm_income$Drug<-"Gabapentin"
Gabapentin enacarbil
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Gabapentin Enacarbil")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
enacarbil.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
enacarbil.mlm_income<-as.data.frame(enacarbil.mlm_income)
enacarbil.mlm_income$Drug<-"Gabapentin Enacarbil"
Pregabalin
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Pregabalin")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
pregabalin.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
pregabalin.mlm_income$Drug<-"Pregabalin"
pregabalin.mlm_income<-as.data.frame(pregabalin.mlm_income)
Individual drugs income AAPC
drug_income_mlm<-rbind(gabapentinoids.mlm_income,gabapentin.mlm_income, enacarbil.mlm_income, pregabalin.mlm_income)
datatable(as.data.frame(drug_income_mlm[,c(12,8:11,7)]),caption = "Average annual percentage change by income level", options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
write.csv(drug_income_mlm,"D:/R/midas gaba/R1/Output_11_Sensitivity_2_Subgroup_AAPC_income.csv")
Meta-analysis of DDD/TID by Year
When DDD=0 or extremely small, Country data wont be counted as number of studies in the meta analysis
options(width=800)
library(Rcpp)
library(meta)
library(data.table)
library(dplyr)
set_subzero<- left_join(analy,GDP,by = c("Country") )
set_subzero$DDD_dum=set_subzero$DDD
set_subzero$DDD_dum[set_subzero$DDD==0]<-0.00001
CIs<-pois.approx(set_subzero$DDD_dum, set_subzero$Population*365.25, conf.level = 0.95)
meta.gaba<-cbind(set_subzero,CIs)
meta.gaba$income <- factor(meta.gaba$income, levels =
c("HIC","UMIC","LMIC"))
meta <- function(rho, iseed){
meta.gaba_1<- subset(meta.gaba, Year==rho & Drug==iseed)
m1_var<-metagen(log(meta.gaba_1$rate),
lower = log(meta.gaba_1$lower),
upper = log(meta.gaba_1$upper),
studlab = meta.gaba_1$Country,
sm = "IRLN", method.tau = "DL",
comb.fixed = TRUE,
byvar = meta.gaba_1$income)
print(c(rho, iseed))
print(summary(m1_var), digits=4)
est.by.random<-c("Year", "DDD/TID", "DDD/TID - lower","DDD/TID - upper")
est.by.random$Year<-rho
est.by.random$Drug<-iseed
est.by.random$`DDD/TID`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$TE)))))
est.by.random$`DDD/TID - lower`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$lower)))))
est.by.random$`DDD/TID - upper`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$upper)))))
est.by.random$income<-(t(data.frame(as.list(((summary(m1_var))$byvar)))))
return(c(est.by.random))
}
datin <- expand.grid(rho = unique(meta.gaba$Year), iseed = unique(meta.gaba$Drug))
i <- 1:nrow(datin)
datout <- with(datin,
lapply(i, function(j){meta(rho[j], iseed[j])}))
## [1] 2008 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1047 [0.1045; 0.1049] 0.2 1.8 UMIC
## AUSTRALIA 0.4520 [0.4516; 0.4525] 0.5 1.8 HIC
## AUSTRIA 1.3565 [1.3552; 1.3578] 0.6 1.8 HIC
## BELARUS 0.0043 [0.0042; 0.0043] 0.0 1.8 UMIC
## BELGIUM 0.3377 [0.3371; 0.3382] 0.2 1.8 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0522 [0.0522; 0.0523] 0.5 1.8 UMIC
## BULGARIA 0.0815 [0.0811; 0.0818] 0.0 1.8 UMIC
## CANADA 2.3427 [2.3418; 2.3435] 4.0 1.8 HIC
## CHILE 0.0425 [0.0423; 0.0426] 0.0 1.8 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0340 [0.0339; 0.0341] 0.1 1.8 UMIC
## CROATIA 0.2812 [0.2804; 0.2821] 0.1 1.8 HIC
## CZECH REPUBLIC 0.5075 [0.5068; 0.5082] 0.3 1.8 HIC
## ECUADOR 0.0727 [0.0725; 0.0730] 0.1 1.8 UMIC
## EGYPT 0.0759 [0.0758; 0.0760] 0.3 1.8 LMIC
## ESTONIA 0.0754 [0.0746; 0.0761] 0.0 1.8 HIC
## FINLAND 0.8574 [0.8561; 0.8587] 0.2 1.8 HIC
## FRANCE 1.2957 [1.2952; 1.2961] 4.1 1.8 HIC
## GERMANY 1.1819 [1.1815; 1.1822] 4.9 1.8 HIC
## GREECE 0.8335 [0.8326; 0.8344] 0.5 1.8 HIC
## HUNGARY 0.2226 [0.2221; 0.2230] 0.1 1.8 HIC
## INDIA 0.0320 [0.0319; 0.0320] 2.0 1.8 LMIC
## IRELAND 0.8713 [0.8699; 0.8727] 0.2 1.8 HIC
## ITALY 0.4963 [0.4960; 0.4966] 1.5 1.8 HIC
## JAPAN 0.0512 [0.0511; 0.0513] 0.3 1.8 HIC
## JORDAN 0.0755 [0.0751; 0.0758] 0.0 1.8 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0298 [0.0294; 0.0301] 0.0 1.8 HIC
## LATVIA 0.3321 [0.3308; 0.3334] 0.0 1.8 HIC
## LEBANON 0.2416 [0.2409; 0.2424] 0.1 1.8 UMIC
## LITHUANIA 0.2161 [0.2153; 0.2170] 0.0 1.8 HIC
## LUXEMBOURG 0.8551 [0.8508; 0.8595] 0.0 1.8 HIC
## MEXICO 0.0713 [0.0712; 0.0713] 0.4 1.8 UMIC
## MOROCCO 0.0074 [0.0073; 0.0074] 0.0 1.8 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.9467 [0.9452; 0.9482] 0.2 1.8 HIC
## NORWAY 1.0638 [1.0623; 1.0653] 0.3 1.8 HIC
## PAKISTAN 0.0542 [0.0542; 0.0543] 0.5 1.8 LMIC
## PERU 0.0242 [0.0241; 0.0243] 0.0 1.8 UMIC
## PHILIPPINES 0.0253 [0.0252; 0.0253] 0.1 1.8 LMIC
## POLAND 0.1458 [0.1456; 0.1460] 0.3 1.8 HIC
## PORTUGAL 1.3098 [1.3087; 1.3109] 0.7 1.8 HIC
## PUERTO RICO 3.4911 [3.4879; 3.4943] 0.6 1.8 HIC
## ROMANIA 0.0578 [0.0576; 0.0580] 0.1 1.8 UMIC
## RUSSIA 0.0124 [0.0123; 0.0124] 0.1 1.8 UMIC
## SAUDI ARABIA 0.1059 [0.1057; 0.1061] 0.1 1.8 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.5926 [0.5916; 0.5937] 0.2 1.8 HIC
## SLOVENIA 0.4548 [0.4533; 0.4564] 0.0 1.8 HIC
## SOUTH AFRICA 0.0515 [0.0514; 0.0516] 0.1 1.8 UMIC
## SOUTH KOREA 0.6427 [0.6424; 0.6431] 1.6 1.8 HIC
## SPAIN 1.7420 [1.7413; 1.7426] 4.1 1.8 HIC
## SWEDEN 1.1713 [1.1701; 1.1724] 0.6 1.8 HIC
## SWITZERLAND 0.5662 [0.5653; 0.5671] 0.2 1.8 HIC
## TUNISIA 0.0646 [0.0643; 0.0648] 0.0 1.8 LMIC
## TÜRKIYE 1.1747 [1.1743; 1.1751] 4.3 1.8 UMIC
## UNITED ARAB EMIRATES 0.1001 [0.0997; 0.1005] 0.0 1.8 HIC
## UNITED KINGDOM 1.5821 [1.5816; 1.5826] 5.1 1.8 HIC
## UNITED STATES 3.7600 [3.7596; 3.7603] 58.7 1.8 HIC
## URUGUAY 0.1957 [0.1949; 0.1964] 0.0 1.8 HIC
## VENEZUELA 0.4496 [0.4492; 0.4500] 0.6 1.8 UMIC
##
## Number of studies combined: k = 57
##
## rate 95%-CI z p-value
## Common effect model 1.9978 [1.9976; 1.9979] 18445.51 0
## Random effects model 0.2173 [0.1533; 0.3080] -8.58 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.8048 [1.4041; 4.5582]; tau = 1.3434 [1.1849; 2.1350]
## I^2 = 100.0%; H = 3836.32
##
## Test of heterogeneity:
## Q d.f. p-value
## 824170594.83 56 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 36 2.5147 [2.5145; 2.5149] 288651868.81 100.0%
## income = UMIC 15 0.4942 [0.4941; 0.4944] 80877794.31 100.0%
## income = LMIC 6 0.0379 [0.0379; 0.0379] 2343735.07 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 452297196.64 2 0
## Within groups 371873398.19 54 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 36 0.4811 [0.3596; 0.6435] 0.7928 0.8904
## income = UMIC 15 0.0677 [0.0279; 0.1641] 3.0622 1.7499
## income = LMIC 6 0.0341 [0.0234; 0.0498] 0.2228 0.4720
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 122.69 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1194 [0.1193; 0.1196] 0.2 1.8 UMIC
## AUSTRALIA 0.4712 [0.4708; 0.4717] 0.5 1.8 HIC
## AUSTRIA 1.4838 [1.4824; 1.4852] 0.6 1.8 HIC
## BELARUS 0.0086 [0.0085; 0.0087] 0.0 1.8 UMIC
## BELGIUM 0.3566 [0.3561; 0.3572] 0.2 1.8 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0590 [0.0590; 0.0591] 0.5 1.8 UMIC
## BULGARIA 0.1115 [0.1111; 0.1119] 0.0 1.8 UMIC
## CANADA 2.4939 [2.4930; 2.4948] 3.8 1.8 HIC
## CHILE 0.0335 [0.0333; 0.0336] 0.0 1.8 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0287 [0.0286; 0.0288] 0.1 1.8 UMIC
## CROATIA 0.2875 [0.2866; 0.2883] 0.1 1.8 HIC
## CZECH REPUBLIC 0.6465 [0.6457; 0.6473] 0.3 1.8 HIC
## ECUADOR 0.0696 [0.0693; 0.0698] 0.0 1.8 UMIC
## EGYPT 0.0833 [0.0832; 0.0834] 0.3 1.8 LMIC
## ESTONIA 0.1091 [0.1081; 0.1100] 0.0 1.8 HIC
## FINLAND 0.8634 [0.8621; 0.8647] 0.2 1.8 HIC
## FRANCE 1.2258 [1.2253; 1.2263] 3.5 1.8 HIC
## GERMANY 1.2946 [1.2942; 1.2950] 4.8 1.8 HIC
## GREECE 0.7983 [0.7974; 0.7991] 0.4 1.8 HIC
## HUNGARY 0.2690 [0.2685; 0.2695] 0.1 1.8 HIC
## INDIA 0.0343 [0.0343; 0.0343] 1.9 1.8 LMIC
## IRELAND 0.8635 [0.8621; 0.8650] 0.2 1.8 HIC
## ITALY 0.4523 [0.4521; 0.4526] 1.2 1.8 HIC
## JAPAN 0.0746 [0.0745; 0.0747] 0.4 1.8 HIC
## JORDAN 0.0886 [0.0882; 0.0889] 0.0 1.8 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0266 [0.0263; 0.0269] 0.0 1.8 HIC
## LATVIA 0.3719 [0.3705; 0.3732] 0.0 1.8 HIC
## LEBANON 0.3655 [0.3646; 0.3664] 0.1 1.8 UMIC
## LITHUANIA 0.2201 [0.2192; 0.2209] 0.0 1.8 HIC
## LUXEMBOURG 0.7788 [0.7747; 0.7829] 0.0 1.8 HIC
## MEXICO 0.0705 [0.0704; 0.0706] 0.4 1.8 UMIC
## MOROCCO 0.0051 [0.0050; 0.0051] 0.0 1.8 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.1319 [1.1303; 1.1336] 0.2 1.8 HIC
## NORWAY 1.3734 [1.3717; 1.3752] 0.3 1.8 HIC
## PAKISTAN 0.0466 [0.0465; 0.0466] 0.4 1.8 LMIC
## PERU 0.0173 [0.0173; 0.0174] 0.0 1.8 UMIC
## PHILIPPINES 0.0257 [0.0256; 0.0257] 0.1 1.8 LMIC
## POLAND 0.1655 [0.1653; 0.1657] 0.3 1.8 HIC
## PORTUGAL 1.1282 [1.1271; 1.1292] 0.5 1.8 HIC
## PUERTO RICO 4.4908 [4.4872; 4.4944] 0.7 1.8 HIC
## ROMANIA 0.1519 [0.1516; 0.1522] 0.1 1.8 UMIC
## RUSSIA 0.0142 [0.0142; 0.0143] 0.1 1.8 UMIC
## SAUDI ARABIA 0.1008 [0.1006; 0.1010] 0.1 1.8 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.7135 [0.7123; 0.7146] 0.2 1.8 HIC
## SLOVENIA 0.4132 [0.4117; 0.4146] 0.0 1.8 HIC
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.8 UMIC
## SOUTH KOREA 0.6597 [0.6593; 0.6601] 1.5 1.8 HIC
## SPAIN 1.7776 [1.7770; 1.7782] 3.8 1.8 HIC
## SWEDEN 1.1817 [1.1805; 1.1828] 0.5 1.8 HIC
## SWITZERLAND 0.5391 [0.5383; 0.5400] 0.2 1.8 HIC
## TUNISIA 0.0831 [0.0828; 0.0834] 0.0 1.8 LMIC
## TÜRKIYE 1.4324 [1.4320; 1.4329] 4.7 1.8 UMIC
## UNITED ARAB EMIRATES 0.1115 [0.1111; 0.1119] 0.0 1.8 HIC
## UNITED KINGDOM 1.8943 [1.8937; 1.8949] 5.4 1.8 HIC
## UNITED STATES 4.2928 [4.2924; 4.2932] 60.0 1.8 HIC
## URUGUAY 0.2686 [0.2677; 0.2695] 0.0 1.8 HIC
## VENEZUELA 0.4848 [0.4844; 0.4852] 0.6 1.8 UMIC
##
## Number of studies combined: k = 57
##
## rate 95%-CI z p-value
## Common effect model 2.2875 [2.2874; 2.2877] 23414.94 0
## Random effects model 0.2370 [0.1660; 0.3382] -7.93 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.8774 [1.4275; 4.6666]; tau = 1.3702 [1.1948; 2.1602]
## I^2 = 100.0%; H = 4104.68
##
## Test of heterogeneity:
## Q d.f. p-value
## 943508416.66 56 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 36 2.8637 [2.8635; 2.8639] 349219719.30 100.0%
## income = UMIC 15 0.6204 [0.6203; 0.6206] 97686744.09 100.0%
## income = LMIC 6 0.0394 [0.0393; 0.0394] 2345064.89 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 494256888.37 2 0
## Within groups 449251528.28 54 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 36 0.5153 [0.3790; 0.7007] 0.8848 0.9406
## income = UMIC 15 0.0802 [0.0323; 0.1992] 3.2317 1.7977
## income = LMIC 6 0.0336 [0.0231; 0.0489] 0.2206 0.4696
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 124.04 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1292 [0.1290; 0.1294] 0.2 1.8 UMIC
## AUSTRALIA 0.5168 [0.5163; 0.5173] 0.4 1.8 HIC
## AUSTRIA 1.6312 [1.6298; 1.6327] 0.5 1.8 HIC
## BELARUS 0.0114 [0.0113; 0.0116] 0.0 1.8 UMIC
## BELGIUM 0.4360 [0.4353; 0.4366] 0.2 1.8 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0614 [0.0614; 0.0615] 0.5 1.8 UMIC
## BULGARIA 0.1533 [0.1528; 0.1537] 0.0 1.8 UMIC
## CANADA 2.6522 [2.6513; 2.6531] 3.5 1.8 HIC
## CHILE 0.0269 [0.0268; 0.0271] 0.0 1.8 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0235 [0.0234; 0.0236] 0.0 1.8 UMIC
## CROATIA 0.2576 [0.2568; 0.2584] 0.0 1.8 HIC
## CZECH REPUBLIC 0.7936 [0.7927; 0.7945] 0.3 1.8 HIC
## ECUADOR 0.0720 [0.0718; 0.0723] 0.0 1.8 UMIC
## EGYPT 0.1069 [0.1067; 0.1070] 0.3 1.8 LMIC
## ESTONIA 0.2425 [0.2411; 0.2439] 0.0 1.8 HIC
## FINLAND 0.8922 [0.8908; 0.8935] 0.2 1.8 HIC
## FRANCE 1.1895 [1.1890; 1.1899] 2.9 1.8 HIC
## GERMANY 1.3619 [1.3615; 1.3624] 4.2 1.8 HIC
## GREECE 0.8032 [0.8023; 0.8041] 0.3 1.8 HIC
## HUNGARY 0.3479 [0.3473; 0.3485] 0.1 1.8 HIC
## INDIA 0.0373 [0.0373; 0.0373] 1.8 1.8 LMIC
## IRELAND 0.8131 [0.8117; 0.8145] 0.1 1.8 HIC
## ITALY 0.4598 [0.4595; 0.4600] 1.0 1.8 HIC
## JAPAN 0.0912 [0.0911; 0.0913] 0.5 1.8 HIC
## JORDAN 0.1086 [0.1082; 0.1090] 0.0 1.8 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0240 [0.0237; 0.0243] 0.0 1.8 HIC
## LATVIA 0.4715 [0.4699; 0.4730] 0.0 1.8 HIC
## LEBANON 0.3637 [0.3629; 0.3646] 0.1 1.8 UMIC
## LITHUANIA 0.2291 [0.2282; 0.2300] 0.0 1.8 HIC
## LUXEMBOURG 0.7427 [0.7388; 0.7466] 0.0 1.8 HIC
## MEXICO 0.0781 [0.0780; 0.0782] 0.3 1.8 UMIC
## MOROCCO 0.0060 [0.0060; 0.0061] 0.0 1.8 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.3538 [1.3520; 1.3556] 0.2 1.8 HIC
## NORWAY 1.7433 [1.7414; 1.7452] 0.3 1.8 HIC
## PAKISTAN 0.0446 [0.0445; 0.0446] 0.3 1.8 LMIC
## PERU 0.0172 [0.0171; 0.0173] 0.0 1.8 UMIC
## PHILIPPINES 0.0279 [0.0278; 0.0279] 0.1 1.8 LMIC
## POLAND 0.1895 [0.1893; 0.1898] 0.3 1.8 HIC
## PORTUGAL 1.1503 [1.1493; 1.1514] 0.5 1.8 HIC
## PUERTO RICO 5.0569 [5.0530; 5.0607] 0.7 1.8 HIC
## ROMANIA 0.2429 [0.2425; 0.2432] 0.2 1.8 UMIC
## RUSSIA 0.0150 [0.0150; 0.0150] 0.1 1.8 UMIC
## SAUDI ARABIA 0.1232 [0.1230; 0.1234] 0.1 1.8 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.8688 [0.8675; 0.8701] 0.2 1.8 HIC
## SLOVENIA 0.4079 [0.4064; 0.4093] 0.0 1.8 HIC
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.8 UMIC
## SOUTH KOREA 0.6761 [0.6757; 0.6765] 1.3 1.8 HIC
## SPAIN 1.7783 [1.7777; 1.7789] 3.2 1.8 HIC
## SWEDEN 1.2220 [1.2209; 1.2232] 0.4 1.8 HIC
## SWITZERLAND 0.5223 [0.5215; 0.5232] 0.2 1.8 HIC
## TUNISIA 0.0815 [0.0812; 0.0818] 0.0 1.8 LMIC
## TÜRKIYE 1.7669 [1.7664; 1.7674] 4.9 1.8 UMIC
## UNITED ARAB EMIRATES 0.1305 [0.1301; 0.1309] 0.0 1.8 HIC
## UNITED KINGDOM 2.2485 [2.2479; 2.2491] 5.5 1.8 HIC
## UNITED STATES 5.2868 [5.2864; 5.2873] 62.9 1.8 HIC
## URUGUAY 0.2401 [0.2392; 0.2409] 0.0 1.8 HIC
## VENEZUELA 0.4784 [0.4780; 0.4788] 0.5 1.8 UMIC
##
## Number of studies combined: k = 57
##
## rate 95%-CI z p-value
## Common effect model 2.8251 [2.8249; 2.8253] 32000.00 0
## Random effects model 0.2603 [0.1795; 0.3773] -7.10 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.0462 [1.4713; 4.8892]; tau = 1.4304 [1.2130; 2.2112]
## I^2 = 100.0%; H = 4538.53
##
## Test of heterogeneity:
## Q d.f. p-value
## 1153500233.89 56 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 36 3.5118 [3.5115; 3.5120] 458765172.43 100.0%
## income = UMIC 15 0.7924 [0.7923; 0.7926] 118801130.26 100.0%
## income = LMIC 6 0.0435 [0.0435; 0.0436] 3598106.74 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 572335824.46 2 0
## Within groups 581164409.43 54 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 36 0.5641 [0.4032; 0.7892] 1.0564 1.0278
## income = UMIC 15 0.0891 [0.0344; 0.2306] 3.5364 1.8805
## income = LMIC 6 0.0367 [0.0236; 0.0570] 0.3034 0.5509
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 96.25 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1364 [0.1363; 0.1366] 0.2 1.6 UMIC
## AUSTRALIA 0.5373 [0.5368; 0.5378] 0.4 1.6 HIC
## AUSTRIA 1.7451 [1.7437; 1.7466] 0.5 1.6 HIC
## BELARUS 0.0127 [0.0126; 0.0129] 0.0 1.6 UMIC
## BELGIUM 0.4774 [0.4767; 0.4781] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0443 [0.0439; 0.0447] 0.0 1.6 UMIC
## BRAZIL 0.0770 [0.0769; 0.0770] 0.5 1.6 UMIC
## BULGARIA 0.2117 [0.2112; 0.2123] 0.1 1.6 UMIC
## CANADA 2.8504 [2.8495; 2.8514] 3.5 1.6 HIC
## CHILE 0.0234 [0.0233; 0.0235] 0.0 1.6 HIC
## CHINA 0.0001 [0.0001; 0.0001] 0.0 1.6 UMIC
## COLOMBIA 0.0219 [0.0218; 0.0220] 0.0 1.6 UMIC
## CROATIA 0.2202 [0.2194; 0.2209] 0.0 1.6 HIC
## CZECH REPUBLIC 0.9490 [0.9480; 0.9500] 0.4 1.6 HIC
## ECUADOR 0.0672 [0.0670; 0.0675] 0.0 1.6 UMIC
## EGYPT 0.1148 [0.1147; 0.1149] 0.3 1.6 LMIC
## ESTONIA 0.2902 [0.2887; 0.2917] 0.0 1.6 HIC
## FINLAND 0.9585 [0.9571; 0.9599] 0.2 1.6 HIC
## FRANCE 1.1489 [1.1484; 1.1493] 2.6 1.6 HIC
## GERMANY 1.4462 [1.4458; 1.4466] 4.2 1.6 HIC
## GREECE 0.8916 [0.8906; 0.8925] 0.3 1.6 HIC
## HUNGARY 0.4195 [0.4188; 0.4201] 0.1 1.6 HIC
## INDIA 0.0430 [0.0430; 0.0430] 1.9 1.6 LMIC
## IRELAND 0.8592 [0.8578; 0.8606] 0.1 1.6 HIC
## ITALY 0.4616 [0.4613; 0.4619] 1.0 1.6 HIC
## JAPAN 0.0780 [0.0779; 0.0781] 0.4 1.6 HIC
## JORDAN 0.1083 [0.1079; 0.1087] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0189 [0.0187; 0.0190] 0.0 1.6 UMIC
## KUWAIT 0.0285 [0.0282; 0.0288] 0.0 1.6 HIC
## LATVIA 0.6091 [0.6073; 0.6108] 0.0 1.6 HIC
## LEBANON 0.3338 [0.3330; 0.3346] 0.1 1.6 UMIC
## LITHUANIA 0.2346 [0.2337; 0.2355] 0.0 1.6 HIC
## LUXEMBOURG 0.7418 [0.7379; 0.7456] 0.0 1.6 HIC
## MEXICO 0.0770 [0.0769; 0.0771] 0.3 1.6 UMIC
## MOROCCO 0.0104 [0.0103; 0.0104] 0.0 1.6 LMIC
## NETHERLANDS 0.6259 [0.6253; 0.6265] 0.4 1.6 HIC
## NEW ZEALAND 1.5656 [1.5637; 1.5675] 0.2 1.6 HIC
## NORWAY 1.9692 [1.9672; 1.9713] 0.4 1.6 HIC
## PAKISTAN 0.0465 [0.0465; 0.0466] 0.3 1.6 LMIC
## PERU 0.0192 [0.0192; 0.0193] 0.0 1.6 UMIC
## PHILIPPINES 0.0269 [0.0269; 0.0270] 0.1 1.6 LMIC
## POLAND 0.2183 [0.2181; 0.2186] 0.3 1.6 HIC
## PORTUGAL 1.1030 [1.1020; 1.1041] 0.4 1.6 HIC
## PUERTO RICO 6.7742 [6.7697; 6.7787] 0.9 1.6 HIC
## ROMANIA 0.3943 [0.3939; 0.3948] 0.3 1.6 UMIC
## RUSSIA 0.0172 [0.0172; 0.0172] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1683 [0.1681; 0.1686] 0.2 1.6 HIC
## SERBIA 0.0660 [0.0657; 0.0663] 0.0 1.6 UMIC
## SLOVAKIA 0.9581 [0.9568; 0.9595] 0.2 1.6 HIC
## SLOVENIA 0.3702 [0.3688; 0.3715] 0.0 1.6 HIC
## SOUTH AFRICA 0.0503 [0.0502; 0.0504] 0.1 1.6 UMIC
## SOUTH KOREA 0.6730 [0.6727; 0.6734] 1.2 1.6 HIC
## SPAIN 1.8210 [1.8204; 1.8217] 3.1 1.6 HIC
## SWEDEN 1.3546 [1.3534; 1.3559] 0.5 1.6 HIC
## SWITZERLAND 0.4905 [0.4897; 0.4913] 0.1 1.6 HIC
## TUNISIA 0.0832 [0.0829; 0.0834] 0.0 1.6 LMIC
## TÜRKIYE 2.1893 [2.1888; 2.1899] 5.8 1.6 UMIC
## UNITED ARAB EMIRATES 0.1306 [0.1302; 0.1310] 0.0 1.6 HIC
## UNITED KINGDOM 2.5925 [2.5919; 2.5932] 6.0 1.6 HIC
## UNITED STATES 5.4299 [5.4295; 5.4303] 61.0 1.6 HIC
## URUGUAY 0.3039 [0.3029; 0.3049] 0.0 1.6 HIC
## VENEZUELA 0.5095 [0.5091; 0.5100] 0.5 1.6 UMIC
##
## Number of studies combined: k = 62
##
## rate 95%-CI z p-value
## Common effect model 2.8937 [2.8935; 2.8939] 33827.91 0
## Random effects model 0.2303 [0.1621; 0.3272] -8.19 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.9913 [1.5846; 5.0935]; tau = 1.4111 [1.2588; 2.2569]
## I^2 = 100.0%; H = 4513.61
##
## Test of heterogeneity:
## Q d.f. p-value
## 1242730575.27 61 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 3.6196 [3.6193; 3.6198] 479648503.25 100.0%
## income = UMIC 19 0.9876 [0.9874; 0.9878] 155618259.22 100.0%
## income = LMIC 6 0.0485 [0.0485; 0.0485] 3636483.50 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 603827329.30 2 0
## Within groups 638903245.96 59 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.6054 [0.4373; 0.8382] 1.0196 1.0098
## income = UMIC 19 0.0601 [0.0248; 0.1459] 3.8932 1.9731
## income = LMIC 6 0.0418 [0.0273; 0.0639] 0.2814 0.5305
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 103.78 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1383 [0.1382; 0.1385] 0.2 1.6 UMIC
## AUSTRALIA 0.5989 [0.5983; 0.5994] 0.5 1.6 HIC
## AUSTRIA 1.8635 [1.8620; 1.8650] 0.6 1.6 HIC
## BELARUS 0.0138 [0.0137; 0.0139] 0.0 1.6 UMIC
## BELGIUM 0.5579 [0.5572; 0.5586] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0958 [0.0953; 0.0964] 0.0 1.6 UMIC
## BRAZIL 0.0876 [0.0875; 0.0876] 0.6 1.6 UMIC
## BULGARIA 0.2763 [0.2756; 0.2769] 0.1 1.6 UMIC
## CANADA 3.1491 [3.1481; 3.1501] 3.9 1.6 HIC
## CHILE 0.0220 [0.0219; 0.0221] 0.0 1.6 HIC
## CHINA 0.0008 [0.0008; 0.0008] 0.0 1.6 UMIC
## COLOMBIA 0.0193 [0.0192; 0.0193] 0.0 1.6 UMIC
## CROATIA 0.1967 [0.1960; 0.1973] 0.0 1.6 HIC
## CZECH REPUBLIC 1.0584 [1.0574; 1.0594] 0.4 1.6 HIC
## ECUADOR 0.0590 [0.0588; 0.0592] 0.0 1.6 UMIC
## EGYPT 0.1044 [0.1043; 0.1045] 0.3 1.6 LMIC
## ESTONIA 0.3901 [0.3883; 0.3919] 0.0 1.6 HIC
## FINLAND 1.0969 [1.0955; 1.0984] 0.2 1.6 HIC
## FRANCE 1.1678 [1.1674; 1.1683] 2.6 1.6 HIC
## GERMANY 1.5091 [1.5087; 1.5096] 4.3 1.6 HIC
## GREECE 0.8239 [0.8230; 0.8248] 0.3 1.6 HIC
## HUNGARY 0.4616 [0.4609; 0.4623] 0.2 1.6 HIC
## INDIA 0.0472 [0.0472; 0.0473] 2.1 1.6 LMIC
## IRELAND 0.8862 [0.8847; 0.8876] 0.1 1.6 HIC
## ITALY 0.4417 [0.4414; 0.4420] 0.9 1.6 HIC
## JAPAN 0.0673 [0.0672; 0.0673] 0.3 1.6 HIC
## JORDAN 0.0998 [0.0994; 0.1002] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0218 [0.0217; 0.0220] 0.0 1.6 UMIC
## KUWAIT 0.0292 [0.0289; 0.0295] 0.0 1.6 HIC
## LATVIA 0.8136 [0.8115; 0.8156] 0.1 1.6 HIC
## LEBANON 0.2992 [0.2985; 0.3000] 0.1 1.6 UMIC
## LITHUANIA 0.2580 [0.2571; 0.2590] 0.0 1.6 HIC
## LUXEMBOURG 0.7475 [0.7437; 0.7514] 0.0 1.6 HIC
## MEXICO 0.0837 [0.0836; 0.0837] 0.3 1.6 UMIC
## MOROCCO 0.0172 [0.0171; 0.0173] 0.0 1.6 LMIC
## NETHERLANDS 0.6326 [0.6320; 0.6332] 0.4 1.6 HIC
## NEW ZEALAND 1.8369 [1.8348; 1.8390] 0.3 1.6 HIC
## NORWAY 2.0990 [2.0969; 2.1011] 0.4 1.6 HIC
## PAKISTAN 0.0469 [0.0469; 0.0470] 0.3 1.6 LMIC
## PERU 0.0190 [0.0189; 0.0191] 0.0 1.6 UMIC
## PHILIPPINES 0.0259 [0.0258; 0.0259] 0.1 1.6 LMIC
## POLAND 0.2025 [0.2022; 0.2027] 0.3 1.6 HIC
## PORTUGAL 1.1794 [1.1784; 1.1805] 0.4 1.6 HIC
## PUERTO RICO 7.9185 [7.9136; 7.9233] 1.0 1.6 HIC
## ROMANIA 0.5050 [0.5044; 0.5055] 0.4 1.6 UMIC
## RUSSIA 0.0208 [0.0208; 0.0209] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1279 [0.1277; 0.1281] 0.1 1.6 HIC
## SERBIA 0.0827 [0.0823; 0.0830] 0.0 1.6 UMIC
## SLOVAKIA 1.0475 [1.0461; 1.0489] 0.2 1.6 HIC
## SLOVENIA 0.3544 [0.3531; 0.3558] 0.0 1.6 HIC
## SOUTH AFRICA 0.0532 [0.0531; 0.0533] 0.1 1.6 UMIC
## SOUTH KOREA 0.7229 [0.7225; 0.7232] 1.3 1.6 HIC
## SPAIN 1.7714 [1.7708; 1.7720] 2.9 1.6 HIC
## SWEDEN 1.4777 [1.4764; 1.4790] 0.5 1.6 HIC
## SWITZERLAND 0.4722 [0.4714; 0.4730] 0.1 1.6 HIC
## TUNISIA 0.1025 [0.1022; 0.1028] 0.0 1.6 LMIC
## TÜRKIYE 2.3690 [2.3685; 2.3696] 6.2 1.6 UMIC
## UNITED ARAB EMIRATES 0.0810 [0.0807; 0.0813] 0.0 1.6 HIC
## UNITED KINGDOM 3.0781 [3.0773; 3.0788] 7.0 1.6 HIC
## UNITED STATES 5.3428 [5.3424; 5.3432] 58.8 1.6 HIC
## URUGUAY 0.4181 [0.4169; 0.4192] 0.0 1.6 HIC
## VENEZUELA 0.5864 [0.5860; 0.5869] 0.6 1.6 UMIC
##
## Number of studies combined: k = 62
##
## rate 95%-CI z p-value
## Common effect model 2.8782 [2.8780; 2.8784] 34136.66 0
## Random effects model 0.2526 [0.1791; 0.3565] -7.83 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.9128 [1.6321; 5.1897]; tau = 1.3831 [1.2775; 2.2781]
## I^2 = 100.0%; H = 4574.18
##
## Test of heterogeneity:
## Q d.f. p-value
## 1276309773.71 61 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 3.6316 [3.6314; 3.6319] 457792055.21 100.0%
## income = UMIC 19 1.0459 [1.0456; 1.0461] 184341041.58 100.0%
## income = LMIC 6 0.0507 [0.0507; 0.0508] 2708095.74 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 631468581.18 2 0
## Within groups 644841192.53 59 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.6282 [0.4616; 0.8549] 0.9147 0.9564
## income = UMIC 19 0.0730 [0.0296; 0.1799] 4.0253 2.0063
## income = LMIC 6 0.0468 [0.0326; 0.0673] 0.2056 0.4535
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 119.28 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1348 [0.1346; 0.1350] 0.2 1.6 UMIC
## AUSTRALIA 0.5535 [0.5530; 0.5540] 0.4 1.6 HIC
## AUSTRIA 1.9645 [1.9629; 1.9660] 0.5 1.6 HIC
## BELARUS 0.0202 [0.0200; 0.0203] 0.0 1.6 UMIC
## BELGIUM 0.7855 [0.7846; 0.7863] 0.3 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.1360 [0.1353; 0.1366] 0.0 1.6 UMIC
## BRAZIL 0.0978 [0.0977; 0.0978] 0.6 1.6 UMIC
## BULGARIA 0.3885 [0.3878; 0.3893] 0.1 1.6 UMIC
## CANADA 3.5072 [3.5062; 3.5082] 3.8 1.6 HIC
## CHILE 0.0206 [0.0205; 0.0207] 0.0 1.6 HIC
## CHINA 0.0011 [0.0011; 0.0012] 0.0 1.6 UMIC
## COLOMBIA 0.0168 [0.0167; 0.0169] 0.0 1.6 UMIC
## CROATIA 0.1815 [0.1809; 0.1822] 0.0 1.6 HIC
## CZECH REPUBLIC 1.2043 [1.2032; 1.2054] 0.4 1.6 HIC
## ECUADOR 0.0490 [0.0488; 0.0492] 0.0 1.6 UMIC
## EGYPT 0.1103 [0.1102; 0.1104] 0.3 1.6 LMIC
## ESTONIA 0.5081 [0.5061; 0.5101] 0.0 1.6 HIC
## FINLAND 1.3146 [1.3131; 1.3162] 0.2 1.6 HIC
## FRANCE 1.1775 [1.1770; 1.1779] 2.3 1.6 HIC
## GERMANY 1.5911 [1.5907; 1.5916] 4.0 1.6 HIC
## GREECE 0.9298 [0.9289; 0.9308] 0.3 1.6 HIC
## HUNGARY 0.5341 [0.5334; 0.5349] 0.2 1.6 HIC
## INDIA 0.0518 [0.0517; 0.0518] 2.0 1.6 LMIC
## IRELAND 0.9313 [0.9299; 0.9328] 0.1 1.6 HIC
## ITALY 0.4364 [0.4362; 0.4367] 0.8 1.6 HIC
## JAPAN 0.0619 [0.0618; 0.0620] 0.2 1.6 HIC
## JORDAN 0.1073 [0.1069; 0.1076] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0198 [0.0197; 0.0199] 0.0 1.6 UMIC
## KUWAIT 0.0408 [0.0404; 0.0411] 0.0 1.6 HIC
## LATVIA 1.0223 [1.0200; 1.0246] 0.1 1.6 HIC
## LEBANON 0.3187 [0.3180; 0.3195] 0.1 1.6 UMIC
## LITHUANIA 0.3056 [0.3046; 0.3067] 0.0 1.6 HIC
## LUXEMBOURG 0.7067 [0.7030; 0.7104] 0.0 1.6 HIC
## MEXICO 0.0904 [0.0903; 0.0905] 0.3 1.6 UMIC
## MOROCCO 0.0270 [0.0269; 0.0271] 0.0 1.6 LMIC
## NETHERLANDS 0.6322 [0.6315; 0.6328] 0.3 1.6 HIC
## NEW ZEALAND 2.0865 [2.0843; 2.0887] 0.3 1.6 HIC
## NORWAY 2.1846 [2.1825; 2.1867] 0.3 1.6 HIC
## PAKISTAN 0.0450 [0.0449; 0.0450] 0.3 1.6 LMIC
## PERU 0.0181 [0.0180; 0.0182] 0.0 1.6 UMIC
## PHILIPPINES 0.0320 [0.0320; 0.0321] 0.1 1.6 LMIC
## POLAND 0.2615 [0.2612; 0.2618] 0.3 1.6 HIC
## PORTUGAL 1.2065 [1.2054; 1.2076] 0.4 1.6 HIC
## PUERTO RICO 8.0802 [8.0752; 8.0851] 0.9 1.6 HIC
## ROMANIA 0.6815 [0.6809; 0.6821] 0.4 1.6 UMIC
## RUSSIA 0.0279 [0.0278; 0.0279] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1192 [0.1190; 0.1194] 0.1 1.6 HIC
## SERBIA 0.0894 [0.0891; 0.0897] 0.0 1.6 UMIC
## SLOVAKIA 1.1680 [1.1665; 1.1695] 0.2 1.6 HIC
## SLOVENIA 0.3501 [0.3488; 0.3515] 0.0 1.6 HIC
## SOUTH AFRICA 0.0572 [0.0571; 0.0573] 0.1 1.6 UMIC
## SOUTH KOREA 0.7358 [0.7354; 0.7362] 1.1 1.6 HIC
## SPAIN 1.7877 [1.7871; 1.7884] 2.6 1.6 HIC
## SWEDEN 1.8346 [1.8332; 1.8360] 0.5 1.6 HIC
## SWITZERLAND 0.4674 [0.4666; 0.4682] 0.1 1.6 HIC
## TUNISIA 0.1062 [0.1059; 0.1066] 0.0 1.6 LMIC
## TÜRKIYE 2.5343 [2.5337; 2.5348] 5.9 1.6 UMIC
## UNITED ARAB EMIRATES 0.0828 [0.0825; 0.0831] 0.0 1.6 HIC
## UNITED KINGDOM 3.7048 [3.7040; 3.7055] 7.4 1.6 HIC
## UNITED STATES 6.2335 [6.2331; 6.2340] 60.4 1.6 HIC
## URUGUAY 0.4117 [0.4106; 0.4128] 0.0 1.6 HIC
## VENEZUELA 0.6841 [0.6836; 0.6846] 0.6 1.6 UMIC
##
## Number of studies combined: k = 62
##
## rate 95%-CI z p-value
## Common effect model 3.3680 [3.3679; 3.3682] 41945.22 0
## Random effects model 0.2782 [0.1954; 0.3960] -7.10 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.0116 [1.6713; 5.3614]; tau = 1.4183 [1.2928; 2.3155]
## I^2 = 100.0%; H = 4944.53
##
## Test of heterogeneity:
## Q d.f. p-value
## 1491351417.42 61 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 4.2623 [4.2621; 4.2626] 540901935.35 100.0%
## income = UMIC 19 1.1108 [1.1106; 1.1110] 205366287.99 100.0%
## income = LMIC 6 0.0547 [0.0546; 0.0547] 2630559.51 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 742452634.57 2 0
## Within groups 748898782.85 59 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.6791 [0.4938; 0.9338] 0.9771 0.9885
## income = UMIC 19 0.0823 [0.0339; 0.1997] 3.8840 1.9708
## income = LMIC 6 0.0535 [0.0380; 0.0755] 0.1848 0.4299
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 116.81 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0031 [0.0031; 0.0032] 0.0 1.6 UMIC
## ARGENTINA 0.1286 [0.1284; 0.1288] 0.2 1.6 UMIC
## AUSTRALIA 0.4967 [0.4963; 0.4972] 0.3 1.6 HIC
## AUSTRIA 2.0725 [2.0710; 2.0741] 0.5 1.6 HIC
## BELARUS 0.0288 [0.0286; 0.0290] 0.0 1.6 UMIC
## BELGIUM 0.9866 [0.9856; 0.9875] 0.3 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.1421 [0.1414; 0.1427] 0.0 1.6 UMIC
## BRAZIL 0.1063 [0.1062; 0.1064] 0.6 1.6 UMIC
## BULGARIA 0.4757 [0.4748; 0.4765] 0.1 1.6 UMIC
## CANADA 3.7037 [3.7027; 3.7048] 3.7 1.6 HIC
## CHILE 0.0202 [0.0201; 0.0203] 0.0 1.6 HIC
## CHINA 0.0017 [0.0017; 0.0017] 0.1 1.6 UMIC
## COLOMBIA 0.0150 [0.0150; 0.0151] 0.0 1.6 UMIC
## CROATIA 0.1794 [0.1787; 0.1800] 0.0 1.6 HIC
## CZECH REPUBLIC 1.3751 [1.3739; 1.3762] 0.4 1.6 HIC
## ECUADOR 0.0434 [0.0432; 0.0436] 0.0 1.6 UMIC
## EGYPT 0.1243 [0.1242; 0.1244] 0.3 1.6 LMIC
## ESTONIA 0.6371 [0.6349; 0.6394] 0.0 1.6 HIC
## FINLAND 1.5449 [1.5432; 1.5466] 0.2 1.6 HIC
## FRANCE 1.2140 [1.2136; 1.2145] 2.2 1.6 HIC
## GERMANY 1.6326 [1.6321; 1.6331] 3.7 1.6 HIC
## GREECE 0.9639 [0.9629; 0.9649] 0.3 1.6 HIC
## HUNGARY 0.6060 [0.6051; 0.6068] 0.2 1.6 HIC
## INDIA 0.0637 [0.0637; 0.0637] 2.3 1.6 LMIC
## IRELAND 0.9594 [0.9580; 0.9609] 0.1 1.6 HIC
## ITALY 0.4569 [0.4567; 0.4572] 0.8 1.6 HIC
## JAPAN 0.0594 [0.0594; 0.0595] 0.2 1.6 HIC
## JORDAN 0.0884 [0.0880; 0.0887] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0265 [0.0264; 0.0267] 0.0 1.6 UMIC
## KUWAIT 0.1028 [0.1022; 0.1033] 0.0 1.6 HIC
## LATVIA 1.2436 [1.2410; 1.2461] 0.1 1.6 HIC
## LEBANON 0.3372 [0.3365; 0.3380] 0.1 1.6 UMIC
## LITHUANIA 0.3238 [0.3227; 0.3248] 0.0 1.6 HIC
## LUXEMBOURG 0.6660 [0.6625; 0.6696] 0.0 1.6 HIC
## MEXICO 0.0891 [0.0890; 0.0892] 0.3 1.6 UMIC
## MOROCCO 0.0311 [0.0310; 0.0312] 0.0 1.6 LMIC
## NETHERLANDS 0.6710 [0.6703; 0.6716] 0.3 1.6 HIC
## NEW ZEALAND 2.3802 [2.3778; 2.3825] 0.3 1.6 HIC
## NORWAY 2.1633 [2.1612; 2.1654] 0.3 1.6 HIC
## PAKISTAN 0.0423 [0.0422; 0.0423] 0.2 1.6 LMIC
## PERU 0.0158 [0.0157; 0.0159] 0.0 1.6 UMIC
## PHILIPPINES 0.0262 [0.0261; 0.0262] 0.1 1.6 LMIC
## POLAND 0.3269 [0.3266; 0.3272] 0.3 1.6 HIC
## PORTUGAL 1.2477 [1.2466; 1.2488] 0.4 1.6 HIC
## PUERTO RICO 9.8772 [9.8717; 9.8826] 0.9 1.6 HIC
## ROMANIA 0.8271 [0.8265; 0.8278] 0.5 1.6 UMIC
## RUSSIA 0.0340 [0.0339; 0.0340] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1250 [0.1248; 0.1252] 0.1 1.6 HIC
## SERBIA 0.0908 [0.0905; 0.0912] 0.0 1.6 UMIC
## SLOVAKIA 1.3977 [1.3960; 1.3993] 0.2 1.6 HIC
## SLOVENIA 0.3401 [0.3388; 0.3414] 0.0 1.6 HIC
## SOUTH AFRICA 0.0581 [0.0580; 0.0582] 0.1 1.6 UMIC
## SOUTH KOREA 0.8166 [0.8162; 0.8170] 1.1 1.6 HIC
## SPAIN 1.8394 [1.8388; 1.8401] 2.4 1.6 HIC
## SWEDEN 2.2832 [2.2816; 2.2847] 0.6 1.6 HIC
## SWITZERLAND 0.4670 [0.4663; 0.4678] 0.1 1.6 HIC
## TUNISIA 0.1185 [0.1182; 0.1189] 0.0 1.6 LMIC
## TÜRKIYE 2.5435 [2.5429; 2.5441] 5.4 1.6 UMIC
## UNITED ARAB EMIRATES 0.0897 [0.0893; 0.0900] 0.0 1.6 HIC
## UNITED KINGDOM 4.3398 [4.3390; 4.3407] 7.9 1.6 HIC
## UNITED STATES 6.8636 [6.8631; 6.8640] 60.7 1.6 HIC
## URUGUAY 0.4417 [0.4405; 0.4429] 0.0 1.6 HIC
## VENEZUELA 0.8366 [0.8360; 0.8371] 0.7 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 3.6955 [3.6953; 3.6957] 47426.96 0
## Random effects model 0.2825 [0.1978; 0.4035] -6.95 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.0839 [1.7249; 5.5323]; tau = 1.4436 [1.3134; 2.3521]
## I^2 = 100.0%; H = 5228.30
##
## Test of heterogeneity:
## Q d.f. p-value
## 1694775110.50 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 4.7326 [4.7323; 4.7329] 602685940.28 100.0%
## income = UMIC 20 1.1028 [1.1026; 1.1030] 219317166.47 100.0%
## income = LMIC 6 0.0648 [0.0648; 0.0648] 3474325.45 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 869297678.30 2 0
## Within groups 825477432.20 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.7501 [0.5442; 1.0339] 0.9918 0.9959
## income = UMIC 20 0.0752 [0.0323; 0.1751] 3.7186 1.9284
## income = LMIC 6 0.0564 [0.0385; 0.0828] 0.2293 0.4788
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 110.49 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0272 [ 0.0271; 0.0273] 0.0 1.6 UMIC
## ARGENTINA 0.1302 [ 0.1300; 0.1303] 0.1 1.6 UMIC
## AUSTRALIA 0.5227 [ 0.5223; 0.5232] 0.3 1.6 HIC
## AUSTRIA 2.0726 [ 2.0710; 2.0742] 0.4 1.6 HIC
## BELARUS 0.0450 [ 0.0447; 0.0452] 0.0 1.6 UMIC
## BELGIUM 1.0645 [ 1.0635; 1.0655] 0.3 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.1982 [ 0.1974; 0.1990] 0.0 1.6 UMIC
## BRAZIL 0.1118 [ 0.1118; 0.1119] 0.6 1.6 UMIC
## BULGARIA 0.5305 [ 0.5296; 0.5314] 0.1 1.6 UMIC
## CANADA 3.9897 [ 3.9886; 3.9908] 3.5 1.6 HIC
## CHILE 0.0193 [ 0.0192; 0.0194] 0.0 1.6 HIC
## CHINA 0.0024 [ 0.0024; 0.0024] 0.1 1.6 UMIC
## COLOMBIA 0.0150 [ 0.0149; 0.0150] 0.0 1.6 UMIC
## CROATIA 0.1488 [ 0.1482; 0.1494] 0.0 1.6 HIC
## CZECH REPUBLIC 1.4960 [ 1.4948; 1.4972] 0.4 1.6 HIC
## ECUADOR 0.0448 [ 0.0447; 0.0450] 0.0 1.6 UMIC
## EGYPT 0.1340 [ 0.1339; 0.1342] 0.3 1.6 LMIC
## ESTONIA 0.7565 [ 0.7540; 0.7589] 0.0 1.6 HIC
## FINLAND 1.7915 [ 1.7896; 1.7934] 0.2 1.6 HIC
## FRANCE 1.2453 [ 1.2449; 1.2458] 2.0 1.6 HIC
## GERMANY 1.6264 [ 1.6260; 1.6269] 3.2 1.6 HIC
## GREECE 1.0152 [ 1.0142; 1.0162] 0.3 1.6 HIC
## HUNGARY 0.6617 [ 0.6609; 0.6625] 0.2 1.6 HIC
## INDIA 0.0748 [ 0.0748; 0.0749] 2.4 1.6 LMIC
## IRELAND 1.0244 [ 1.0229; 1.0259] 0.1 1.6 HIC
## ITALY 0.4675 [ 0.4672; 0.4678] 0.7 1.6 HIC
## JAPAN 0.0546 [ 0.0545; 0.0547] 0.2 1.6 HIC
## JORDAN 0.0728 [ 0.0725; 0.0731] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0249 [ 0.0247; 0.0250] 0.0 1.6 UMIC
## KUWAIT 0.1504 [ 0.1497; 0.1510] 0.0 1.6 HIC
## LATVIA 1.5285 [ 1.5257; 1.5314] 0.1 1.6 HIC
## LEBANON 0.3553 [ 0.3545; 0.3560] 0.1 1.6 UMIC
## LITHUANIA 0.3623 [ 0.3611; 0.3634] 0.0 1.6 HIC
## LUXEMBOURG 0.6946 [ 0.6910; 0.6982] 0.0 1.6 HIC
## MEXICO 0.0935 [ 0.0934; 0.0936] 0.3 1.6 UMIC
## MOROCCO 0.0421 [ 0.0420; 0.0423] 0.0 1.6 LMIC
## NETHERLANDS 0.6953 [ 0.6947; 0.6960] 0.3 1.6 HIC
## NEW ZEALAND 2.7866 [ 2.7840; 2.7891] 0.3 1.6 HIC
## NORWAY 2.6161 [ 2.6138; 2.6184] 0.3 1.6 HIC
## PAKISTAN 0.0398 [ 0.0397; 0.0398] 0.2 1.6 LMIC
## PERU 0.0558 [ 0.0557; 0.0560] 0.0 1.6 UMIC
## PHILIPPINES 0.0264 [ 0.0263; 0.0264] 0.1 1.6 LMIC
## POLAND 0.3937 [ 0.3933; 0.3940] 0.4 1.6 HIC
## PORTUGAL 1.2963 [ 1.2951; 1.2974] 0.3 1.6 HIC
## PUERTO RICO 11.3817 [11.3758; 11.3877] 0.9 1.6 HIC
## ROMANIA 0.9393 [ 0.9386; 0.9400] 0.5 1.6 UMIC
## RUSSIA 0.0434 [ 0.0433; 0.0435] 0.2 1.6 UMIC
## SAUDI ARABIA 0.2044 [ 0.2042; 0.2047] 0.2 1.6 HIC
## SERBIA 0.1135 [ 0.1131; 0.1138] 0.0 1.6 UMIC
## SLOVAKIA 1.5471 [ 1.5454; 1.5489] 0.2 1.6 HIC
## SLOVENIA 0.3335 [ 0.3322; 0.3348] 0.0 1.6 HIC
## SOUTH AFRICA 0.0551 [ 0.0550; 0.0552] 0.1 1.6 UMIC
## SOUTH KOREA 0.8380 [ 0.8375; 0.8384] 1.0 1.6 HIC
## SPAIN 1.9208 [ 1.9201; 1.9214] 2.2 1.6 HIC
## SWEDEN 2.6703 [ 2.6686; 2.6720] 0.6 1.6 HIC
## SWITZERLAND 0.4692 [ 0.4685; 0.4700] 0.1 1.6 HIC
## TUNISIA 0.0928 [ 0.0925; 0.0931] 0.0 1.6 LMIC
## TÜRKIYE 2.6042 [ 2.6036; 2.6048] 5.0 1.6 UMIC
## UNITED ARAB EMIRATES 0.1007 [ 0.1004; 0.1011] 0.0 1.6 HIC
## UNITED KINGDOM 4.9631 [ 4.9622; 4.9640] 8.0 1.6 HIC
## UNITED STATES 8.0519 [ 8.0514; 8.0524] 62.9 1.6 HIC
## URUGUAY 0.3638 [ 0.3627; 0.3649] 0.0 1.6 HIC
## VENEZUELA 0.3521 [ 0.3517; 0.3524] 0.3 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 4.3381 [4.3378; 4.3383] 56847.55 0
## Random effects model 0.3185 [0.2194; 0.4624] -6.02 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.2794 [1.7890; 5.7921]; tau = 1.5098 [1.3375; 2.4067]
## I^2 = 100.0%; H = 5713.28
##
## Test of heterogeneity:
## Q d.f. p-value
## 2023779940.10 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 5.5742 [5.5739; 5.5745] 723854014.36 100.0%
## income = UMIC 20 1.0656 [1.0654; 1.0658] 247913241.08 100.0%
## income = LMIC 6 0.0741 [0.0741; 0.0741] 3952893.78 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1048059790.89 2 0
## Within groups 975720149.21 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.8145 [0.5824; 1.1391] 1.0837 1.0410
## income = UMIC 20 0.0931 [0.0374; 0.2319] 4.3374 2.0826
## income = LMIC 6 0.0588 [0.0395; 0.0874] 0.2464 0.4964
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 103.19 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0677 [ 0.0676; 0.0678] 0.1 1.6 UMIC
## ARGENTINA 0.1252 [ 0.1251; 0.1254] 0.1 1.6 UMIC
## AUSTRALIA 0.5211 [ 0.5206; 0.5216] 0.3 1.6 HIC
## AUSTRIA 2.0123 [ 2.0108; 2.0139] 0.4 1.6 HIC
## BELARUS 0.0506 [ 0.0504; 0.0509] 0.0 1.6 UMIC
## BELGIUM 0.9463 [ 0.9454; 0.9473] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.2626 [ 0.2617; 0.2635] 0.0 1.6 UMIC
## BRAZIL 0.1122 [ 0.1121; 0.1123] 0.5 1.6 UMIC
## BULGARIA 0.5447 [ 0.5438; 0.5456] 0.1 1.6 UMIC
## CANADA 4.4020 [ 4.4008; 4.4031] 3.5 1.6 HIC
## CHILE 0.0195 [ 0.0194; 0.0196] 0.0 1.6 HIC
## CHINA 0.0029 [ 0.0029; 0.0029] 0.1 1.6 UMIC
## COLOMBIA 0.0147 [ 0.0147; 0.0148] 0.0 1.6 UMIC
## CROATIA 0.1381 [ 0.1375; 0.1387] 0.0 1.6 HIC
## CZECH REPUBLIC 1.5516 [ 1.5503; 1.5528] 0.4 1.6 HIC
## ECUADOR 0.0484 [ 0.0483; 0.0486] 0.0 1.6 UMIC
## EGYPT 0.1568 [ 0.1567; 0.1570] 0.3 1.6 LMIC
## ESTONIA 0.8935 [ 0.8908; 0.8961] 0.0 1.6 HIC
## FINLAND 2.1176 [ 2.1156; 2.1196] 0.3 1.6 HIC
## FRANCE 1.2728 [ 1.2724; 1.2733] 1.8 1.6 HIC
## GERMANY 1.6335 [ 1.6330; 1.6339] 3.0 1.6 HIC
## GREECE 1.0272 [ 1.0262; 1.0283] 0.2 1.6 HIC
## HUNGARY 0.7096 [ 0.7088; 0.7105] 0.2 1.6 HIC
## INDIA 0.0872 [ 0.0871; 0.0872] 2.5 1.6 LMIC
## IRELAND 1.0758 [ 1.0743; 1.0774] 0.1 1.6 HIC
## ITALY 0.4614 [ 0.4611; 0.4616] 0.6 1.6 HIC
## JAPAN 0.0506 [ 0.0506; 0.0507] 0.1 1.6 HIC
## JORDAN 0.0856 [ 0.0852; 0.0859] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0249 [ 0.0247; 0.0250] 0.0 1.6 UMIC
## KUWAIT 0.4524 [ 0.4513; 0.4535] 0.0 1.6 HIC
## LATVIA 1.6478 [ 1.6449; 1.6508] 0.1 1.6 HIC
## LEBANON 0.4366 [ 0.4357; 0.4374] 0.1 1.6 UMIC
## LITHUANIA 0.4147 [ 0.4135; 0.4159] 0.0 1.6 HIC
## LUXEMBOURG 0.6676 [ 0.6641; 0.6710] 0.0 1.6 HIC
## MEXICO 0.1015 [ 0.1014; 0.1016] 0.3 1.6 UMIC
## MOROCCO 0.0514 [ 0.0512; 0.0515] 0.0 1.6 LMIC
## NETHERLANDS 0.7283 [ 0.7277; 0.7290] 0.3 1.6 HIC
## NEW ZEALAND 3.2800 [ 3.2773; 3.2827] 0.3 1.6 HIC
## NORWAY 2.6999 [ 2.6976; 2.7022] 0.3 1.6 HIC
## PAKISTAN 0.0364 [ 0.0364; 0.0365] 0.2 1.6 LMIC
## PERU 0.0566 [ 0.0565; 0.0568] 0.0 1.6 UMIC
## PHILIPPINES 0.0264 [ 0.0263; 0.0264] 0.1 1.6 LMIC
## POLAND 0.4345 [ 0.4341; 0.4348] 0.4 1.6 HIC
## PORTUGAL 1.3293 [ 1.3281; 1.3304] 0.3 1.6 HIC
## PUERTO RICO 12.7832 [12.7768; 12.7896] 0.9 1.6 HIC
## ROMANIA 1.0924 [ 1.0917; 1.0932] 0.5 1.6 UMIC
## RUSSIA 0.0910 [ 0.0909; 0.0911] 0.3 1.6 UMIC
## SAUDI ARABIA 0.4083 [ 0.4079; 0.4087] 0.3 1.6 HIC
## SERBIA 0.1056 [ 0.1052; 0.1060] 0.0 1.6 UMIC
## SLOVAKIA 1.4818 [ 1.4801; 1.4835] 0.2 1.6 HIC
## SLOVENIA 0.3248 [ 0.3235; 0.3261] 0.0 1.6 HIC
## SOUTH AFRICA 0.0642 [ 0.0640; 0.0643] 0.1 1.6 UMIC
## SOUTH KOREA 0.8851 [ 0.8847; 0.8855] 1.0 1.6 HIC
## SPAIN 2.0159 [ 2.0152; 2.0166] 2.1 1.6 HIC
## SWEDEN 3.3238 [ 3.3219; 3.3257] 0.7 1.6 HIC
## SWITZERLAND 0.4696 [ 0.4688; 0.4703] 0.1 1.6 HIC
## TUNISIA 0.0930 [ 0.0927; 0.0933] 0.0 1.6 LMIC
## TÜRKIYE 2.4860 [ 2.4854; 2.4865] 4.4 1.6 UMIC
## UNITED ARAB EMIRATES 0.0726 [ 0.0723; 0.0729] 0.0 1.6 HIC
## UNITED KINGDOM 5.6024 [ 5.6015; 5.6034] 8.2 1.6 HIC
## UNITED STATES 9.0106 [ 9.0101; 9.0112] 64.0 1.6 HIC
## URUGUAY 0.3608 [ 0.3598; 0.3619] 0.0 1.6 HIC
## VENEZUELA 0.0491 [ 0.0490; 0.0493] 0.0 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 4.8485 [4.8482; 4.8487] 64361.90 0
## Random effects model 0.3410 [0.2324; 0.5002] -5.50 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.4090 [1.8215; 5.9390]; tau = 1.5521 [1.3496; 2.4370]
## I^2 = 100.0%; H = 6111.85
##
## Test of heterogeneity:
## Q d.f. p-value
## 2315991729.64 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 6.2758 [6.2755; 6.2761] 821121258.21 100.0%
## income = UMIC 20 0.9712 [0.9710; 0.9714] 255458662.73 100.0%
## income = LMIC 6 0.0860 [0.0860; 0.0860] 5531403.73 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1233880404.97 2 0
## Within groups 1082111324.67 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.8814 [0.6261; 1.2408] 1.1268 1.0615
## income = UMIC 20 0.0977 [0.0395; 0.2415] 4.2686 2.0661
## income = LMIC 6 0.0630 [0.0403; 0.0987] 0.3139 0.5603
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 90.55 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.1275 [ 0.1273; 0.1277] 0.1 1.6 UMIC
## ARGENTINA 0.1241 [ 0.1240; 0.1243] 0.1 1.6 UMIC
## AUSTRALIA 0.5571 [ 0.5567; 0.5576] 0.3 1.6 HIC
## AUSTRIA 1.9951 [ 1.9936; 1.9967] 0.4 1.6 HIC
## BELARUS 0.0798 [ 0.0795; 0.0801] 0.0 1.6 UMIC
## BELGIUM 0.8834 [ 0.8825; 0.8843] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.3009 [ 0.3000; 0.3019] 0.0 1.6 UMIC
## BRAZIL 0.1148 [ 0.1148; 0.1149] 0.5 1.6 UMIC
## BULGARIA 0.5112 [ 0.5103; 0.5120] 0.1 1.6 UMIC
## CANADA 4.6415 [ 4.6404; 4.6427] 3.4 1.6 HIC
## CHILE 0.0198 [ 0.0197; 0.0200] 0.0 1.6 HIC
## CHINA 0.0036 [ 0.0036; 0.0036] 0.1 1.6 UMIC
## COLOMBIA 0.0119 [ 0.0118; 0.0119] 0.0 1.6 UMIC
## CROATIA 0.1228 [ 0.1223; 0.1234] 0.0 1.6 HIC
## CZECH REPUBLIC 1.5995 [ 1.5983; 1.6008] 0.3 1.6 HIC
## ECUADOR 0.0520 [ 0.0519; 0.0522] 0.0 1.6 UMIC
## EGYPT 0.1489 [ 0.1488; 0.1491] 0.3 1.6 LMIC
## ESTONIA 1.1053 [ 1.1024; 1.1083] 0.0 1.6 HIC
## FINLAND 2.5507 [ 2.5485; 2.5529] 0.3 1.6 HIC
## FRANCE 1.2946 [ 1.2941; 1.2951] 1.7 1.6 HIC
## GERMANY 1.6232 [ 1.6228; 1.6237] 2.7 1.6 HIC
## GREECE 1.0026 [ 1.0016; 1.0036] 0.2 1.6 HIC
## HUNGARY 0.7423 [ 0.7414; 0.7432] 0.1 1.6 HIC
## INDIA 0.0989 [ 0.0989; 0.0989] 2.6 1.6 LMIC
## IRELAND 1.1361 [ 1.1345; 1.1377] 0.1 1.6 HIC
## ITALY 0.4773 [ 0.4770; 0.4776] 0.6 1.6 HIC
## JAPAN 0.0479 [ 0.0478; 0.0479] 0.1 1.6 HIC
## JORDAN 0.1192 [ 0.1189; 0.1196] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0220 [ 0.0219; 0.0221] 0.0 1.6 UMIC
## KUWAIT 0.9078 [ 0.9062; 0.9093] 0.1 1.6 HIC
## LATVIA 1.9407 [ 1.9375; 1.9439] 0.1 1.6 HIC
## LEBANON 0.4970 [ 0.4962; 0.4979] 0.1 1.6 UMIC
## LITHUANIA 0.5030 [ 0.5017; 0.5044] 0.0 1.6 HIC
## LUXEMBOURG 0.6295 [ 0.6261; 0.6328] 0.0 1.6 HIC
## MEXICO 0.0994 [ 0.0993; 0.0995] 0.2 1.6 UMIC
## MOROCCO 0.0369 [ 0.0368; 0.0370] 0.0 1.6 LMIC
## NETHERLANDS 0.7541 [ 0.7534; 0.7548] 0.3 1.6 HIC
## NEW ZEALAND 3.6822 [ 3.6794; 3.6851] 0.3 1.6 HIC
## NORWAY 2.9019 [ 2.8995; 2.9043] 0.3 1.6 HIC
## PAKISTAN 0.0311 [ 0.0310; 0.0311] 0.1 1.6 LMIC
## PERU 0.0733 [ 0.0732; 0.0735] 0.0 1.6 UMIC
## PHILIPPINES 0.0235 [ 0.0235; 0.0236] 0.0 1.6 LMIC
## POLAND 0.4565 [ 0.4562; 0.4569] 0.3 1.6 HIC
## PORTUGAL 1.3906 [ 1.3894; 1.3918] 0.3 1.6 HIC
## PUERTO RICO 14.4217 [14.4148; 14.4287] 0.9 1.6 HIC
## ROMANIA 1.2827 [ 1.2819; 1.2835] 0.5 1.6 UMIC
## RUSSIA 0.1499 [ 0.1498; 0.1500] 0.4 1.6 UMIC
## SAUDI ARABIA 0.5669 [ 0.5665; 0.5673] 0.4 1.6 HIC
## SERBIA 0.1059 [ 0.1055; 0.1062] 0.0 1.6 UMIC
## SLOVAKIA 1.5497 [ 1.5480; 1.5515] 0.2 1.6 HIC
## SLOVENIA 0.3402 [ 0.3389; 0.3415] 0.0 1.6 HIC
## SOUTH AFRICA 0.0655 [ 0.0654; 0.0656] 0.1 1.6 UMIC
## SOUTH KOREA 0.9343 [ 0.9339; 0.9348] 1.0 1.6 HIC
## SPAIN 2.1164 [ 2.1157; 2.1171] 2.0 1.6 HIC
## SWEDEN 3.8220 [ 3.8200; 3.8240] 0.8 1.6 HIC
## SWITZERLAND 0.4715 [ 0.4708; 0.4723] 0.1 1.6 HIC
## TUNISIA 0.0891 [ 0.0888; 0.0894] 0.0 1.6 LMIC
## TÜRKIYE 2.5680 [ 2.5674; 2.5685] 4.2 1.6 UMIC
## UNITED ARAB EMIRATES 0.0593 [ 0.0590; 0.0595] 0.0 1.6 HIC
## UNITED KINGDOM 5.9464 [ 5.9454; 5.9474] 7.9 1.6 HIC
## UNITED STATES 10.0405 [10.0399; 10.0410] 65.0 1.6 HIC
## URUGUAY 0.3376 [ 0.3365; 0.3386] 0.0 1.6 HIC
## VENEZUELA 0.0634 [ 0.0633; 0.0636] 0.0 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 5.3743 [5.3741; 5.3746] 71997.49 0
## Random effects model 0.3671 [0.2480; 0.5435] -5.01 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.5233 [1.8336; 5.9963]; tau = 1.5885 [1.3541; 2.4487]
## I^2 = 100.0%; H = 6494.18
##
## Test of heterogeneity:
## Q d.f. p-value
## 2614808302.37 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 7.0035 [7.0031; 7.0038] 925115335.67 100.0%
## income = UMIC 20 0.9686 [0.9685; 0.9688] 274970212.78 100.0%
## income = LMIC 6 0.0949 [0.0949; 0.0949] 6278560.94 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1408444192.98 2 0
## Within groups 1206364109.40 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.9413 [0.6624; 1.3374] 1.1886 1.0902
## income = UMIC 20 0.1123 [0.0469; 0.2688] 3.9661 1.9915
## income = LMIC 6 0.0573 [0.0350; 0.0937] 0.3780 0.6149
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 88.70 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0899 [ 0.0898; 0.0901] 0.1 1.6 UMIC
## ARGENTINA 0.1258 [ 0.1257; 0.1260] 0.1 1.6 UMIC
## AUSTRALIA 0.6078 [ 0.6073; 0.6083] 0.3 1.6 HIC
## AUSTRIA 1.9698 [ 1.9682; 1.9713] 0.3 1.6 HIC
## BELARUS 0.1144 [ 0.1140; 0.1148] 0.0 1.6 UMIC
## BELGIUM 0.8532 [ 0.8523; 0.8541] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.3650 [ 0.3640; 0.3661] 0.0 1.6 UMIC
## BRAZIL 0.1201 [ 0.1201; 0.1202] 0.5 1.6 UMIC
## BULGARIA 0.4250 [ 0.4242; 0.4258] 0.1 1.6 UMIC
## CANADA 4.7363 [ 4.7351; 4.7374] 3.4 1.6 HIC
## CHILE 0.0214 [ 0.0213; 0.0215] 0.0 1.6 HIC
## CHINA 0.0045 [ 0.0045; 0.0045] 0.1 1.6 UMIC
## COLOMBIA 0.0146 [ 0.0145; 0.0146] 0.0 1.6 UMIC
## CROATIA 0.1161 [ 0.1155; 0.1166] 0.0 1.6 HIC
## CZECH REPUBLIC 1.6328 [ 1.6316; 1.6341] 0.3 1.6 HIC
## ECUADOR 0.0512 [ 0.0510; 0.0513] 0.0 1.6 UMIC
## EGYPT 0.1647 [ 0.1646; 0.1648] 0.3 1.6 LMIC
## ESTONIA 1.2831 [ 1.2799; 1.2862] 0.0 1.6 HIC
## FINLAND 2.9751 [ 2.9727; 2.9775] 0.3 1.6 HIC
## FRANCE 1.3025 [ 1.3020; 1.3029] 1.6 1.6 HIC
## GERMANY 1.6220 [ 1.6215; 1.6224] 2.6 1.6 HIC
## GREECE 0.9888 [ 0.9878; 0.9898] 0.2 1.6 HIC
## HUNGARY 0.8046 [ 0.8036; 0.8055] 0.2 1.6 HIC
## INDIA 0.1112 [ 0.1112; 0.1112] 2.9 1.6 LMIC
## IRELAND 1.1971 [ 1.1955; 1.1987] 0.1 1.6 HIC
## ITALY 0.4811 [ 0.4808; 0.4814] 0.6 1.6 HIC
## JAPAN 0.0469 [ 0.0469; 0.0470] 0.1 1.6 HIC
## JORDAN 0.3329 [ 0.3323; 0.3335] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0243 [ 0.0242; 0.0244] 0.0 1.6 UMIC
## KUWAIT 0.9165 [ 0.9150; 0.9180] 0.1 1.6 HIC
## LATVIA 2.2963 [ 2.2928; 2.2999] 0.1 1.6 HIC
## LEBANON 0.4987 [ 0.4978; 0.4996] 0.1 1.6 UMIC
## LITHUANIA 0.5684 [ 0.5669; 0.5699] 0.0 1.6 HIC
## LUXEMBOURG 0.6434 [ 0.6400; 0.6467] 0.0 1.6 HIC
## MEXICO 0.1003 [ 0.1002; 0.1004] 0.2 1.6 UMIC
## MOROCCO 0.0402 [ 0.0401; 0.0403] 0.0 1.6 LMIC
## NETHERLANDS 0.7875 [ 0.7868; 0.7882] 0.3 1.6 HIC
## NEW ZEALAND 3.9262 [ 3.9233; 3.9292] 0.4 1.6 HIC
## NORWAY 3.1466 [ 3.1441; 3.1490] 0.3 1.6 HIC
## PAKISTAN 0.0328 [ 0.0328; 0.0328] 0.1 1.6 LMIC
## PERU 0.0703 [ 0.0701; 0.0704] 0.0 1.6 UMIC
## PHILIPPINES 0.0290 [ 0.0289; 0.0290] 0.1 1.6 LMIC
## POLAND 0.4943 [ 0.4940; 0.4947] 0.4 1.6 HIC
## PORTUGAL 1.4597 [ 1.4585; 1.4609] 0.3 1.6 HIC
## PUERTO RICO 13.5416 [13.5347; 13.5484] 0.8 1.6 HIC
## ROMANIA 1.3647 [ 1.3638; 1.3656] 0.5 1.6 UMIC
## RUSSIA 0.2081 [ 0.2079; 0.2082] 0.6 1.6 UMIC
## SAUDI ARABIA 0.7810 [ 0.7805; 0.7815] 0.5 1.6 HIC
## SERBIA 0.1066 [ 0.1063; 0.1070] 0.0 1.6 UMIC
## SLOVAKIA 1.6975 [ 1.6957; 1.6993] 0.2 1.6 HIC
## SLOVENIA 0.3713 [ 0.3700; 0.3727] 0.0 1.6 HIC
## SOUTH AFRICA 0.0662 [ 0.0661; 0.0663] 0.1 1.6 UMIC
## SOUTH KOREA 0.9307 [ 0.9303; 0.9312] 0.9 1.6 HIC
## SPAIN 2.1714 [ 2.1707; 2.1721] 2.0 1.6 HIC
## SWEDEN 4.3111 [ 4.3090; 4.3133] 0.8 1.6 HIC
## SWITZERLAND 0.4772 [ 0.4765; 0.4780] 0.1 1.6 HIC
## TUNISIA 0.0896 [ 0.0893; 0.0899] 0.0 1.6 LMIC
## TÜRKIYE 2.5078 [ 2.5072; 2.5084] 4.0 1.6 UMIC
## UNITED ARAB EMIRATES 0.0566 [ 0.0563; 0.0568] 0.0 1.6 HIC
## UNITED KINGDOM 5.9441 [ 5.9431; 5.9450] 7.7 1.6 HIC
## UNITED STATES 10.2334 [10.2328; 10.2340] 64.8 1.6 HIC
## URUGUAY 0.3050 [ 0.3040; 0.3059] 0.0 1.6 HIC
## VENEZUELA 0.0258 [ 0.0257; 0.0259] 0.0 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 5.3754 [5.3751; 5.3756] 73051.87 0
## Random effects model 0.3866 [0.2603; 0.5743] -4.71 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.5676 [1.8034; 5.8545]; tau = 1.6024 [1.3429; 2.4196]
## I^2 = 100.0%; H = 6665.68
##
## Test of heterogeneity:
## Q d.f. p-value
## 2754744100.30 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 7.1112 [7.1109; 7.1116] 944185492.90 100.0%
## income = UMIC 20 0.9212 [0.9211; 0.9214] 278313597.21 100.0%
## income = LMIC 6 0.1064 [0.1063; 0.1064] 7178813.36 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1525066196.83 2 0
## Within groups 1229677903.47 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.9833 [0.6926; 1.3959] 1.1827 1.0875
## income = UMIC 20 0.1185 [0.0509; 0.2755] 3.7090 1.9259
## income = LMIC 6 0.0630 [0.0384; 0.1036] 0.3848 0.6203
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 85.52 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0000 0.0 0.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0000 0.0 0.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 0
##
## rate 95%-CI z p-value
## Common effect model NA -- --
## Random effects model NA -- --
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups NA 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2009 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0000 0.0 0.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0000 0.0 0.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 0
##
## rate 95%-CI z p-value
## Common effect model NA -- --
## Random effects model NA -- --
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups NA 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2010 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0000 0.0 0.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0000 0.0 0.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 0
##
## rate 95%-CI z p-value
## Common effect model NA -- --
## Random effects model NA -- --
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups NA 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 0 NA -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2011 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0000 0.0 0.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0038 [0.0037; 0.0038] 100.0 100.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 1
##
## rate 95%-CI z p-value
## Common effect model 0.0038 [0.0037; 0.0038] -3650.95 0
## Random effects model 0.0038 [0.0037; 0.0038] -3650.95 0
##
## Quantifying heterogeneity:
## tau^2 = NA; tau = NA; I^2 = NA; H = NA
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 1 0.0038 [0.0037; 0.0038] 0.00 --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 0.00 0 --
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 1 0.0038 [0.0037; 0.0038] -- --
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2012 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0077 [0.0076; 0.0077] 19.7 34.5 HIC
## PUERTO RICO 0.0001 [0.0000; 0.0001] 0.0 31.0 HIC
## UNITED STATES 0.0128 [0.0127; 0.0128] 80.3 34.5 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 3
##
## rate 95%-CI z p-value
## Common effect model 0.0115 [0.0115; 0.0116] -6024.99 0
## Random effects model 0.0020 [0.0013; 0.0030] -29.07 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.1334 [0.0822; 64.1210]; tau = 0.3652 [0.2866; 8.0076]
## I^2 = 100.0%; H = 196.20
##
## Test of heterogeneity:
## Q d.f. p-value
## 76989.32 2 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 3 0.0115 [0.0115; 0.0116] 76989.32 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 76989.32 2 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 3 0.0020 [0.0013; 0.0030] 0.1334 0.3652
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0242 [0.0241; 0.0242] 45.5 33.6 HIC
## PUERTO RICO 0.0001 [0.0001; 0.0001] 0.0 32.8 HIC
## UNITED STATES 0.0117 [0.0117; 0.0118] 54.5 33.6 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 3
##
## rate 95%-CI z p-value
## Common effect model 0.0163 [0.0163; 0.0163] -6495.82 0
## Random effects model 0.0034 [0.0019; 0.0060] -19.14 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.2637 [0.1048; 77.7515]; tau = 0.5135 [0.3237; 8.8177]
## I^2 = 100.0%; H = 403.58
##
## Test of heterogeneity:
## Q d.f. p-value
## 325758.76 2 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 3 0.0163 [0.0163; 0.0163] 325758.76 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 325758.76 2 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 3 0.0034 [0.0019; 0.0060] 0.2637 0.5135
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0342 [0.0341; 0.0342] 38.8 50.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0216 [0.0216; 0.0217] 61.2 50.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0258 [0.0258; 0.0259] -7418.82 0
## Random effects model 0.0272 [0.0174; 0.0425] -15.80 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.1041; tau = 0.3227; I^2 = 100.0%; H = 451.27
##
## Test of heterogeneity:
## Q d.f. p-value
## 203648.74 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 2 0.0258 [0.0258; 0.0259] 203648.74 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 203648.74 1 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 2 0.0272 [0.0174; 0.0425] 0.1041 0.3227
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2015 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0381 [0.0381; 0.0382] 31.0 41.3 HIC
## PUERTO RICO 0.0001 [0.0001; 0.0001] 0.0 17.5 HIC
## UNITED STATES 0.0339 [0.0338; 0.0339] 69.0 41.3 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 3
##
## rate 95%-CI z p-value
## Common effect model 0.0351 [0.0351; 0.0352] -8031.15 0
## Random effects model 0.0122 [0.0108; 0.0136] -74.89 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0084 [0.0458; 45.9506]; tau = 0.0917 [0.2139; 6.7787]
## I^2 = 100.0%; H = 101.68
##
## Test of heterogeneity:
## Q d.f. p-value
## 20677.27 2 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 3 0.0351 [0.0351; 0.0352] 20677.27 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 20677.27 2 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 3 0.0122 [0.0108; 0.0136] 0.0084 0.0917
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0411 [0.0410; 0.0411] 25.2 50.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0482 [0.0481; 0.0482] 74.8 50.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0463 [0.0462; 0.0463] -8471.76 0
## Random effects model 0.0445 [0.0380; 0.0520] -39.08 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0127; tau = 0.1126; I^2 = 100.0%; H = 190.69
##
## Test of heterogeneity:
## Q d.f. p-value
## 36363.36 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 2 0.0463 [0.0462; 0.0463] 36363.36 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 36363.36 1 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 2 0.0445 [0.0380; 0.0520] 0.0127 0.1126
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2017 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0434 [0.0433; 0.0435] 25.1 50.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0509 [0.0509; 0.0510] 74.9 50.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0489 [0.0489; 0.0490] -8570.68 0
## Random effects model 0.0470 [0.0402; 0.0550] -38.25 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0128; tau = 0.1131; I^2 = 100.0%; H = 196.78
##
## Test of heterogeneity:
## Q d.f. p-value
## 38720.95 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 2 0.0489 [0.0489; 0.0490] 38720.95 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 38720.95 1 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 2 0.0470 [0.0402; 0.0550] 0.0128 0.1131
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2018 2
## rate 95%-CI %W(common) %W(random) income
## JAPAN 0.0472 [0.0472; 0.0473] 29.4 50.0 HIC
## PUERTO RICO 0.0000 0.0 0.0 HIC
## UNITED STATES 0.0441 [0.0440; 0.0441] 70.6 50.0 HIC
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.0000 0.0 0.0 UMIC
## AUSTRALIA 0.0000 0.0 0.0 HIC
## AUSTRIA 0.0000 0.0 0.0 HIC
## BELARUS 0.0000 0.0 0.0 UMIC
## BELGIUM 0.0000 0.0 0.0 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0000 0.0 0.0 UMIC
## CANADA 0.0000 0.0 0.0 HIC
## CHILE 0.0000 0.0 0.0 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0000 0.0 0.0 UMIC
## CROATIA 0.0000 0.0 0.0 HIC
## CZECH REPUBLIC 0.0000 0.0 0.0 HIC
## ECUADOR 0.0000 0.0 0.0 UMIC
## EGYPT 0.0000 0.0 0.0 LMIC
## ESTONIA 0.0000 0.0 0.0 HIC
## FINLAND 0.0000 0.0 0.0 HIC
## FRANCE 0.0000 0.0 0.0 HIC
## GERMANY 0.0000 0.0 0.0 HIC
## GREECE 0.0000 0.0 0.0 HIC
## HUNGARY 0.0000 0.0 0.0 HIC
## INDIA 0.0000 0.0 0.0 LMIC
## IRELAND 0.0000 0.0 0.0 HIC
## ITALY 0.0000 0.0 0.0 HIC
## JORDAN 0.0000 0.0 0.0 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0000 0.0 0.0 HIC
## LATVIA 0.0000 0.0 0.0 HIC
## LEBANON 0.0000 0.0 0.0 UMIC
## LITHUANIA 0.0000 0.0 0.0 HIC
## LUXEMBOURG 0.0000 0.0 0.0 HIC
## MEXICO 0.0000 0.0 0.0 UMIC
## MOROCCO 0.0000 0.0 0.0 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0000 0.0 0.0 HIC
## NORWAY 0.0000 0.0 0.0 HIC
## PAKISTAN 0.0000 0.0 0.0 LMIC
## PERU 0.0000 0.0 0.0 UMIC
## PHILIPPINES 0.0000 0.0 0.0 LMIC
## POLAND 0.0000 0.0 0.0 HIC
## PORTUGAL 0.0000 0.0 0.0 HIC
## ROMANIA 0.0000 0.0 0.0 UMIC
## RUSSIA 0.0000 0.0 0.0 UMIC
## SAUDI ARABIA 0.0000 0.0 0.0 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.0000 0.0 0.0 HIC
## SLOVENIA 0.0000 0.0 0.0 HIC
## SOUTH AFRICA 0.0000 0.0 0.0 UMIC
## SOUTH KOREA 0.0000 0.0 0.0 HIC
## SPAIN 0.0000 0.0 0.0 HIC
## SWEDEN 0.0000 0.0 0.0 HIC
## SWITZERLAND 0.0000 0.0 0.0 HIC
## TUNISIA 0.0000 0.0 0.0 LMIC
## TÜRKIYE 0.0000 0.0 0.0 UMIC
## UNITED ARAB EMIRATES 0.0000 0.0 0.0 HIC
## UNITED KINGDOM 0.0000 0.0 0.0 HIC
## URUGUAY 0.0000 0.0 0.0 HIC
## VENEZUELA 0.0000 0.0 0.0 UMIC
##
## Number of studies combined: k = 2
##
## rate 95%-CI z p-value
## Common effect model 0.0450 [0.0449; 0.0450] -8471.24 0
## Random effects model 0.0456 [0.0426; 0.0488] -89.48 0
##
## Quantifying heterogeneity:
## tau^2 = 0.0024; tau = 0.0488; I^2 = 100.0%; H = 85.88
##
## Test of heterogeneity:
## Q d.f. p-value
## 7374.78 1 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 2 0.0450 [0.0449; 0.0450] 7374.78 100.0%
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 0.00 0 --
## Within groups 7374.78 1 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 2 0.0456 [0.0426; 0.0488] 0.0024 0.0488
## income = UMIC 0 NA -- --
## income = LMIC 0 NA -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.00 0 --
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Log transformation
## [1] 2008 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0709 [0.0708; 0.0711] 0.2 1.8 UMIC
## ARGENTINA 0.1063 [0.1061; 0.1065] 0.3 1.8 UMIC
## AUSTRALIA 0.3953 [0.3948; 0.3957] 0.6 1.8 HIC
## AUSTRIA 0.6969 [0.6960; 0.6979] 0.4 1.8 HIC
## BELARUS 0.0007 [0.0007; 0.0008] 0.0 1.8 UMIC
## BELGIUM 0.9189 [0.9179; 0.9198] 0.7 1.8 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0176 [0.0175; 0.0178] 0.0 1.8 UMIC
## CANADA 1.5870 [1.5863; 1.5877] 3.6 1.8 HIC
## CHILE 0.1689 [0.1685; 0.1692] 0.2 1.8 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0536 [0.0535; 0.0537] 0.2 1.8 UMIC
## CROATIA 0.0229 [0.0227; 0.0232] 0.0 1.8 HIC
## CZECH REPUBLIC 0.2781 [0.2776; 0.2787] 0.2 1.8 HIC
## ECUADOR 0.1220 [0.1217; 0.1223] 0.1 1.8 UMIC
## EGYPT 0.0305 [0.0305; 0.0306] 0.2 1.8 LMIC
## ESTONIA 0.1266 [0.1256; 0.1276] 0.0 1.8 HIC
## FINLAND 3.1430 [3.1405; 3.1455] 1.1 1.8 HIC
## FRANCE 2.1012 [2.1006; 2.1018] 8.8 1.8 HIC
## GERMANY 1.4550 [1.4545; 1.4554] 7.9 1.8 HIC
## GREECE 1.4149 [1.4138; 1.4161] 1.1 1.8 HIC
## HUNGARY 0.4858 [0.4851; 0.4865] 0.3 1.8 HIC
## INDIA 0.0834 [0.0834; 0.0835] 6.7 1.8 LMIC
## IRELAND 1.8513 [1.8492; 1.8534] 0.5 1.8 HIC
## ITALY 0.8724 [0.8720; 0.8728] 3.5 1.8 HIC
## JAPAN 0.0000 0.0 0.0 HIC
## JORDAN 0.0510 [0.0507; 0.0513] 0.0 1.8 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0877 [0.0871; 0.0883] 0.0 1.8 HIC
## LATVIA 0.0843 [0.0837; 0.0849] 0.0 1.8 HIC
## LEBANON 0.1605 [0.1599; 0.1611] 0.1 1.8 UMIC
## LITHUANIA 0.1279 [0.1273; 0.1286] 0.0 1.8 HIC
## LUXEMBOURG 2.3963 [2.3891; 2.4035] 0.1 1.8 HIC
## MEXICO 0.2075 [0.2074; 0.2077] 1.5 1.8 UMIC
## MOROCCO 0.0162 [0.0161; 0.0163] 0.0 1.8 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0115 [0.0113; 0.0116] 0.0 1.8 HIC
## NORWAY 2.2509 [2.2487; 2.2531] 0.7 1.8 HIC
## PAKISTAN 0.0431 [0.0430; 0.0431] 0.5 1.8 LMIC
## PERU 0.0255 [0.0254; 0.0256] 0.0 1.8 UMIC
## PHILIPPINES 0.0258 [0.0258; 0.0259] 0.2 1.8 LMIC
## POLAND 0.0042 [0.0042; 0.0043] 0.0 1.8 HIC
## PORTUGAL 1.8127 [1.8114; 1.8140] 1.3 1.8 HIC
## PUERTO RICO 1.2283 [1.2264; 1.2302] 0.3 1.8 HIC
## ROMANIA 0.2445 [0.2441; 0.2448] 0.3 1.8 UMIC
## RUSSIA 0.0135 [0.0135; 0.0135] 0.1 1.8 UMIC
## SAUDI ARABIA 0.2557 [0.2553; 0.2560] 0.4 1.8 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.7817 [0.7804; 0.7829] 0.3 1.8 HIC
## SLOVENIA 1.0760 [1.0736; 1.0784] 0.1 1.8 HIC
## SOUTH AFRICA 0.0644 [0.0643; 0.0645] 0.2 1.8 UMIC
## SOUTH KOREA 0.3052 [0.3049; 0.3055] 1.0 1.8 HIC
## SPAIN 2.0244 [2.0237; 2.0250] 6.3 1.8 HIC
## SWEDEN 2.1038 [2.1022; 2.1053] 1.3 1.8 HIC
## SWITZERLAND 1.2317 [1.2304; 1.2330] 0.6 1.8 HIC
## TUNISIA 0.0578 [0.0575; 0.0580] 0.0 1.8 LMIC
## TÜRKIYE 0.2043 [0.2041; 0.2045] 1.0 1.8 UMIC
## UNITED ARAB EMIRATES 0.2101 [0.2095; 0.2106] 0.1 1.8 HIC
## UNITED KINGDOM 1.2962 [1.2957; 1.2966] 5.4 1.8 HIC
## UNITED STATES 2.0094 [2.0091; 2.0096] 41.0 1.8 HIC
## URUGUAY 0.0582 [0.0578; 0.0586] 0.0 1.8 HIC
## VENEZUELA 0.2297 [0.2294; 0.2300] 0.4 1.8 UMIC
##
## Number of studies combined: k = 56
##
## rate 95%-CI z p-value
## Common effect model 1.1940 [1.1939; 1.1941] 4132.42 0
## Random effects model 0.2047 [0.1517; 0.2761] -10.39 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.3054 [1.2107; 3.7463]; tau = 1.1425 [1.1003; 1.9355]
## I^2 = 100.0%; H = 3217.23
##
## Test of heterogeneity:
## Q d.f. p-value
## 569281220.82 55 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 35 1.6845 [1.6844; 1.6847] 81305271.86 100.0%
## income = UMIC 15 0.1538 [0.1538; 0.1539] 10075371.04 100.0%
## income = LMIC 6 0.0756 [0.0756; 0.0756] 3386637.21 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 474513940.72 2 0
## Within groups 94767280.11 53 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 35 0.4709 [0.4021; 0.5513] 0.2268 0.4762
## income = UMIC 15 0.0579 [0.0404; 0.0832] 0.5102 0.7143
## income = LMIC 6 0.0372 [0.0227; 0.0609] 0.3795 0.6160
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 179.03 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.2753 [0.2750; 0.2756] 0.5 1.8 UMIC
## ARGENTINA 0.1721 [0.1719; 0.1723] 0.4 1.8 UMIC
## AUSTRALIA 0.5211 [0.5206; 0.5216] 0.6 1.8 HIC
## AUSTRIA 0.8998 [0.8987; 0.9009] 0.4 1.8 HIC
## BELARUS 0.0047 [0.0047; 0.0048] 0.0 1.8 UMIC
## BELGIUM 1.1686 [1.1675; 1.1696] 0.7 1.8 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0149 [0.0149; 0.0149] 0.2 1.8 UMIC
## BULGARIA 0.0978 [0.0975; 0.0982] 0.0 1.8 UMIC
## CANADA 2.0856 [2.0848; 2.0864] 4.0 1.8 HIC
## CHILE 0.1928 [0.1924; 0.1931] 0.2 1.8 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0553 [0.0552; 0.0554] 0.1 1.8 UMIC
## CROATIA 0.0616 [0.0612; 0.0619] 0.0 1.8 HIC
## CZECH REPUBLIC 0.3950 [0.3944; 0.3956] 0.2 1.8 HIC
## ECUADOR 0.1724 [0.1721; 0.1728] 0.1 1.8 UMIC
## EGYPT 0.0666 [0.0665; 0.0667] 0.3 1.8 LMIC
## ESTONIA 0.1453 [0.1442; 0.1463] 0.0 1.8 HIC
## FINLAND 3.7363 [3.7336; 3.7391] 1.1 1.8 HIC
## FRANCE 2.4658 [2.4652; 2.4665] 8.7 1.8 HIC
## GERMANY 1.7548 [1.7543; 1.7552] 8.0 1.8 HIC
## GREECE 1.8254 [1.8241; 1.8267] 1.1 1.8 HIC
## HUNGARY 0.5582 [0.5574; 0.5589] 0.3 1.8 HIC
## INDIA 0.1226 [0.1226; 0.1226] 8.4 1.8 LMIC
## IRELAND 2.4820 [2.4796; 2.4844] 0.6 1.8 HIC
## ITALY 1.0462 [1.0457; 1.0466] 3.5 1.8 HIC
## JAPAN 0.0000 0.0 0.0 HIC
## JORDAN 0.0665 [0.0662; 0.0669] 0.0 1.8 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1249 [0.1242; 0.1256] 0.0 1.8 HIC
## LATVIA 0.1286 [0.1278; 0.1294] 0.0 1.8 HIC
## LEBANON 0.2201 [0.2194; 0.2208] 0.1 1.8 UMIC
## LITHUANIA 0.1812 [0.1805; 0.1820] 0.0 1.8 HIC
## LUXEMBOURG 2.6071 [2.5997; 2.6145] 0.1 1.8 HIC
## MEXICO 0.2079 [0.2077; 0.2080] 1.3 1.8 UMIC
## MOROCCO 0.0335 [0.0334; 0.0336] 0.1 1.8 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0275 [0.0272; 0.0277] 0.0 1.8 HIC
## NORWAY 2.1133 [2.1111; 2.1154] 0.6 1.8 HIC
## PAKISTAN 0.0910 [0.0909; 0.0911] 0.9 1.8 LMIC
## PERU 0.0375 [0.0374; 0.0376] 0.1 1.8 UMIC
## PHILIPPINES 0.0362 [0.0361; 0.0363] 0.2 1.8 LMIC
## POLAND 0.0070 [0.0070; 0.0070] 0.0 1.8 HIC
## PORTUGAL 2.2539 [2.2525; 2.2554] 1.4 1.8 HIC
## PUERTO RICO 1.2289 [1.2270; 1.2308] 0.2 1.8 HIC
## ROMANIA 0.4241 [0.4236; 0.4245] 0.5 1.8 UMIC
## RUSSIA 0.0280 [0.0280; 0.0281] 0.2 1.8 UMIC
## SAUDI ARABIA 0.3084 [0.3081; 0.3088] 0.5 1.8 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.0966 [1.0951; 1.0980] 0.3 1.8 HIC
## SLOVENIA 1.3641 [1.3615; 1.3668] 0.2 1.8 HIC
## SOUTH AFRICA 0.1130 [0.1129; 0.1132] 0.3 1.8 UMIC
## SOUTH KOREA 0.4698 [0.4694; 0.4701] 1.3 1.8 HIC
## SPAIN 2.5800 [2.5793; 2.5808] 6.8 1.8 HIC
## SWEDEN 2.6356 [2.6338; 2.6373] 1.4 1.8 HIC
## SWITZERLAND 1.4282 [1.4268; 1.4296] 0.6 1.8 HIC
## TUNISIA 0.1176 [0.1172; 0.1179] 0.1 1.8 LMIC
## TÜRKIYE 0.2942 [0.2940; 0.2944] 1.2 1.8 UMIC
## UNITED ARAB EMIRATES 0.3381 [0.3374; 0.3387] 0.2 1.8 HIC
## UNITED KINGDOM 1.7332 [1.7327; 1.7337] 6.2 1.8 HIC
## UNITED STATES 2.0289 [2.0286; 2.0292] 35.1 1.8 HIC
## URUGUAY 0.1756 [0.1748; 0.1763] 0.0 1.8 HIC
## VENEZUELA 0.2484 [0.2480; 0.2487] 0.4 1.8 UMIC
##
## Number of studies combined: k = 57
##
## rate 95%-CI z p-value
## Common effect model 1.2480 [1.2479; 1.2481] 5631.44 0
## Random effects model 0.2936 [0.2189; 0.3939] -8.18 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2799 [1.1540; 3.3969]; tau = 1.1313 [1.0742; 1.8431]
## I^2 = 100.0%; H = 3528.14
##
## Test of heterogeneity:
## Q d.f. p-value
## 697073498.32 56 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 35 1.8737 [1.8735; 1.8738] 85541115.20 100.0%
## income = UMIC 16 0.1882 [0.1881; 0.1882] 20017200.83 100.0%
## income = LMIC 6 0.1135 [0.1135; 0.1135] 3349688.52 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 588165493.77 2 0
## Within groups 108908004.55 54 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 35 0.6350 [0.5479; 0.7359] 0.1981 0.4451
## income = UMIC 16 0.0936 [0.0631; 0.1391] 0.6509 0.8068
## income = LMIC 6 0.0688 [0.0485; 0.0975] 0.1902 0.4361
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 186.93 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.3584 [0.3581; 0.3587] 0.6 1.7 UMIC
## ARGENTINA 0.2832 [0.2829; 0.2835] 0.6 1.7 UMIC
## AUSTRALIA 0.6228 [0.6222; 0.6233] 0.7 1.7 HIC
## AUSTRIA 1.2242 [1.2230; 1.2254] 0.5 1.7 HIC
## BELARUS 0.0055 [0.0055; 0.0056] 0.0 1.7 UMIC
## BELGIUM 1.7016 [1.7003; 1.7029] 0.9 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0598 [0.0597; 0.0598] 0.6 1.7 UMIC
## BULGARIA 0.1600 [0.1596; 0.1605] 0.1 1.7 UMIC
## CANADA 2.4841 [2.4832; 2.4850] 4.2 1.7 HIC
## CHILE 0.2310 [0.2307; 0.2314] 0.2 1.7 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0525 [0.0524; 0.0526] 0.1 1.7 UMIC
## CROATIA 0.1835 [0.1828; 0.1842] 0.0 1.7 HIC
## CZECH REPUBLIC 0.5302 [0.5294; 0.5309] 0.3 1.7 HIC
## ECUADOR 0.2196 [0.2193; 0.2200] 0.2 1.7 UMIC
## EGYPT 0.1329 [0.1327; 0.1330] 0.5 1.7 LMIC
## ESTONIA 0.1949 [0.1936; 0.1961] 0.0 1.7 HIC
## FINLAND 4.1791 [4.1762; 4.1820] 1.1 1.7 HIC
## FRANCE 2.8205 [2.8198; 2.8211] 8.7 1.7 HIC
## GERMANY 2.0536 [2.0531; 2.0541] 8.2 1.7 HIC
## GREECE 2.0220 [2.0206; 2.0234] 1.1 1.7 HIC
## HUNGARY 0.6560 [0.6551; 0.6568] 0.3 1.7 HIC
## INDIA 0.1545 [0.1545; 0.1546] 9.4 1.7 LMIC
## IRELAND 2.9483 [2.9457; 2.9509] 0.7 1.7 HIC
## ITALY 1.2133 [1.2128; 1.2137] 3.5 1.7 HIC
## JAPAN 0.1013 [0.1012; 0.1014] 0.6 1.7 HIC
## JORDAN 0.0748 [0.0744; 0.0751] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1322 [0.1315; 0.1329] 0.0 1.7 HIC
## LATVIA 0.0932 [0.0925; 0.0939] 0.0 1.7 HIC
## LEBANON 0.2822 [0.2814; 0.2830] 0.1 1.7 UMIC
## LITHUANIA 0.2760 [0.2750; 0.2769] 0.0 1.7 HIC
## LUXEMBOURG 2.8977 [2.8900; 2.9055] 0.1 1.7 HIC
## MEXICO 0.2123 [0.2122; 0.2124] 1.2 1.7 UMIC
## MOROCCO 0.0372 [0.0371; 0.0373] 0.1 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0421 [0.0418; 0.0424] 0.0 1.7 HIC
## NORWAY 2.0804 [2.0783; 2.0825] 0.5 1.7 HIC
## PAKISTAN 0.1127 [0.1127; 0.1128] 1.0 1.7 LMIC
## PERU 0.0528 [0.0527; 0.0530] 0.1 1.7 UMIC
## PHILIPPINES 0.0385 [0.0384; 0.0386] 0.2 1.7 LMIC
## POLAND 0.0089 [0.0089; 0.0090] 0.0 1.7 HIC
## PORTUGAL 2.6481 [2.6465; 2.6497] 1.4 1.7 HIC
## PUERTO RICO 1.2056 [1.2038; 1.2075] 0.2 1.7 HIC
## ROMANIA 0.3370 [0.3366; 0.3374] 0.3 1.7 UMIC
## RUSSIA 0.0706 [0.0705; 0.0707] 0.5 1.7 UMIC
## SAUDI ARABIA 0.3637 [0.3633; 0.3641] 0.5 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.4645 [1.4628; 1.4662] 0.4 1.7 HIC
## SLOVENIA 1.6411 [1.6382; 1.6440] 0.2 1.7 HIC
## SOUTH AFRICA 0.1369 [0.1367; 0.1371] 0.3 1.7 UMIC
## SOUTH KOREA 0.5801 [0.5797; 0.5804] 1.4 1.7 HIC
## SPAIN 3.0276 [3.0268; 3.0284] 7.0 1.7 HIC
## SWEDEN 2.9378 [2.9360; 2.9396] 1.4 1.7 HIC
## SWITZERLAND 1.7455 [1.7440; 1.7471] 0.7 1.7 HIC
## TUNISIA 0.1599 [0.1595; 0.1603] 0.1 1.7 LMIC
## TÜRKIYE 0.3858 [0.3855; 0.3860] 1.4 1.7 UMIC
## UNITED ARAB EMIRATES 0.6148 [0.6140; 0.6157] 0.3 1.7 HIC
## UNITED KINGDOM 2.2840 [2.2834; 2.2846] 7.1 1.7 HIC
## UNITED STATES 1.9869 [1.9866; 1.9872] 30.2 1.7 HIC
## URUGUAY 0.3082 [0.3073; 0.3092] 0.1 1.7 HIC
## VENEZUELA 0.2893 [0.2890; 0.2896] 0.4 1.7 UMIC
##
## Number of studies combined: k = 58
##
## rate 95%-CI z p-value
## Common effect model 1.2881 [1.2880; 1.2882] 6901.49 0
## Random effects model 0.3690 [0.2753; 0.4946] -6.67 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2962 [1.1036; 3.1191]; tau = 1.1385 [1.0505; 1.7661]
## I^2 = 100.0%; H = 3836.96
##
## Test of heterogeneity:
## Q d.f. p-value
## 839169439.35 57 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 36 2.0009 [2.0008; 2.0011] 137965705.00 100.0%
## income = UMIC 16 0.2081 [0.2081; 0.2082] 21248975.07 100.0%
## income = LMIC 6 0.1449 [0.1448; 0.1449] 3922775.38 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 676031983.90 2 0
## Within groups 163137455.45 55 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 36 0.7469 [0.6295; 0.8861] 0.2737 0.5232
## income = UMIC 16 0.1282 [0.0905; 0.1817] 0.5060 0.7113
## income = LMIC 6 0.0900 [0.0653; 0.1240] 0.1606 0.4008
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 174.92 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5023 [0.5019; 0.5026] 0.8 1.6 UMIC
## ARGENTINA 0.4871 [0.4868; 0.4875] 0.8 1.6 UMIC
## AUSTRALIA 0.7498 [0.7492; 0.7503] 0.7 1.6 HIC
## AUSTRIA 1.5245 [1.5231; 1.5258] 0.5 1.6 HIC
## BELARUS 0.0049 [0.0048; 0.0050] 0.0 1.6 UMIC
## BELGIUM 2.0732 [2.0718; 2.0746] 1.0 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0047 [0.0046; 0.0048] 0.0 1.6 UMIC
## BRAZIL 0.0849 [0.0848; 0.0849] 0.7 1.6 UMIC
## BULGARIA 0.2639 [0.2633; 0.2645] 0.1 1.6 UMIC
## CANADA 2.7564 [2.7555; 2.7573] 4.0 1.6 HIC
## CHILE 0.2907 [0.2903; 0.2912] 0.2 1.6 HIC
## CHINA 0.0001 [0.0001; 0.0001] 0.0 1.6 UMIC
## COLOMBIA 0.0601 [0.0600; 0.0602] 0.1 1.6 UMIC
## CROATIA 0.2403 [0.2396; 0.2411] 0.0 1.6 HIC
## CZECH REPUBLIC 0.7926 [0.7917; 0.7935] 0.3 1.6 HIC
## ECUADOR 0.2650 [0.2645; 0.2654] 0.2 1.6 UMIC
## EGYPT 0.2101 [0.2100; 0.2103] 0.7 1.6 LMIC
## ESTONIA 0.2309 [0.2295; 0.2322] 0.0 1.6 HIC
## FINLAND 4.4782 [4.4752; 4.4811] 1.0 1.6 HIC
## FRANCE 3.0134 [3.0127; 3.0141] 8.0 1.6 HIC
## GERMANY 2.2494 [2.2488; 2.2499] 7.6 1.6 HIC
## GREECE 2.1845 [2.1831; 2.1860] 1.0 1.6 HIC
## HUNGARY 0.7797 [0.7788; 0.7806] 0.3 1.6 HIC
## INDIA 0.1927 [0.1926; 0.1927] 10.1 1.6 LMIC
## IRELAND 3.6375 [3.6346; 3.6404] 0.7 1.6 HIC
## ITALY 1.3872 [1.3867; 1.3877] 3.5 1.6 HIC
## JAPAN 0.6315 [0.6313; 0.6317] 3.4 1.6 HIC
## JORDAN 0.0969 [0.0965; 0.0972] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0096 [0.0095; 0.0097] 0.0 1.6 UMIC
## KUWAIT 0.1977 [0.1969; 0.1985] 0.0 1.6 HIC
## LATVIA 0.0698 [0.0692; 0.0703] 0.0 1.6 HIC
## LEBANON 0.4274 [0.4265; 0.4283] 0.1 1.6 UMIC
## LITHUANIA 0.4824 [0.4812; 0.4837] 0.1 1.6 HIC
## LUXEMBOURG 3.0533 [3.0455; 3.0612] 0.1 1.6 HIC
## MEXICO 0.1896 [0.1894; 0.1897] 0.9 1.6 UMIC
## MOROCCO 0.0429 [0.0428; 0.0430] 0.1 1.6 LMIC
## NETHERLANDS 1.6853 [1.6843; 1.6863] 1.2 1.6 HIC
## NEW ZEALAND 0.0543 [0.0540; 0.0547] 0.0 1.6 HIC
## NORWAY 2.3768 [2.3745; 2.3790] 0.5 1.6 HIC
## PAKISTAN 0.1482 [0.1481; 0.1483] 1.1 1.6 LMIC
## PERU 0.0621 [0.0619; 0.0622] 0.1 1.6 UMIC
## PHILIPPINES 0.0444 [0.0443; 0.0444] 0.2 1.6 LMIC
## POLAND 0.0169 [0.0168; 0.0170] 0.0 1.6 HIC
## PORTUGAL 2.7574 [2.7557; 2.7591] 1.2 1.6 HIC
## PUERTO RICO 1.2517 [1.2498; 1.2536] 0.2 1.6 HIC
## ROMANIA 0.3103 [0.3099; 0.3107] 0.3 1.6 UMIC
## RUSSIA 0.1395 [0.1394; 0.1396] 0.8 1.6 UMIC
## SAUDI ARABIA 0.4245 [0.4241; 0.4249] 0.5 1.6 HIC
## SERBIA 0.0287 [0.0285; 0.0289] 0.0 1.6 UMIC
## SLOVAKIA 1.9608 [1.9589; 1.9628] 0.4 1.6 HIC
## SLOVENIA 1.8618 [1.8587; 1.8649] 0.2 1.6 HIC
## SOUTH AFRICA 0.1591 [0.1589; 0.1592] 0.3 1.6 UMIC
## SOUTH KOREA 0.6952 [0.6948; 0.6956] 1.4 1.6 HIC
## SPAIN 3.4853 [3.4845; 3.4862] 6.9 1.6 HIC
## SWEDEN 3.1335 [3.1316; 3.1353] 1.2 1.6 HIC
## SWITZERLAND 2.0182 [2.0166; 2.0198] 0.7 1.6 HIC
## TUNISIA 0.1873 [0.1868; 0.1877] 0.1 1.6 LMIC
## TÜRKIYE 0.5211 [0.5209; 0.5214] 1.6 1.6 UMIC
## UNITED ARAB EMIRATES 0.6845 [0.6836; 0.6854] 0.3 1.6 HIC
## UNITED KINGDOM 2.8221 [2.8214; 2.8228] 7.5 1.6 HIC
## UNITED STATES 1.9860 [1.9858; 1.9863] 25.8 1.6 HIC
## URUGUAY 0.3948 [0.3937; 0.3959] 0.1 1.6 HIC
## VENEZUELA 0.3710 [0.3706; 0.3713] 0.4 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 1.3365 [1.3365; 1.3366] 8580.43 0
## Random effects model 0.3432 [0.2626; 0.4485] -7.83 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1754 [1.0609; 2.8737]; tau = 1.0842 [1.0300; 1.6952]
## I^2 = 100.0%; H = 3852.33
##
## Test of heterogeneity:
## Q d.f. p-value
## 920109374.64 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 2.0915 [2.0913; 2.0916] 155089858.38 100.0%
## income = UMIC 20 0.2690 [0.2689; 0.2690] 32521861.20 100.0%
## income = LMIC 6 0.1837 [0.1837; 0.1837] 4988274.97 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 727509380.09 2 0
## Within groups 192599994.55 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.9459 [0.8031; 1.1141] 0.2580 0.5079
## income = UMIC 20 0.0733 [0.0525; 0.1023] 0.5776 0.7600
## income = LMIC 6 0.1135 [0.0834; 0.1545] 0.1483 0.3851
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 269.30 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5436 [0.5432; 0.5440] 0.8 1.6 UMIC
## ARGENTINA 0.6692 [0.6688; 0.6696] 1.0 1.6 UMIC
## AUSTRALIA 0.8732 [0.8725; 0.8738] 0.7 1.6 HIC
## AUSTRIA 1.8363 [1.8348; 1.8378] 0.6 1.6 HIC
## BELARUS 0.0060 [0.0059; 0.0061] 0.0 1.6 UMIC
## BELGIUM 2.1765 [2.1751; 2.1780] 0.9 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0084 [0.0082; 0.0085] 0.0 1.6 UMIC
## BRAZIL 0.1127 [0.1126; 0.1128] 0.8 1.6 UMIC
## BULGARIA 0.0620 [0.0617; 0.0623] 0.0 1.6 UMIC
## CANADA 3.0424 [3.0415; 3.0434] 3.9 1.6 HIC
## CHILE 0.3808 [0.3804; 0.3813] 0.2 1.6 HIC
## CHINA 0.0004 [0.0004; 0.0004] 0.0 1.6 UMIC
## COLOMBIA 0.0711 [0.0710; 0.0712] 0.1 1.6 UMIC
## CROATIA 0.3116 [0.3107; 0.3125] 0.0 1.6 HIC
## CZECH REPUBLIC 0.7861 [0.7852; 0.7869] 0.3 1.6 HIC
## ECUADOR 0.3125 [0.3120; 0.3129] 0.2 1.6 UMIC
## EGYPT 0.3476 [0.3474; 0.3478] 1.1 1.6 LMIC
## ESTONIA 0.2598 [0.2584; 0.2613] 0.0 1.6 HIC
## FINLAND 4.7950 [4.7920; 4.7981] 1.0 1.6 HIC
## FRANCE 3.3218 [3.3211; 3.3226] 7.8 1.6 HIC
## GERMANY 2.4802 [2.4797; 2.4808] 7.4 1.6 HIC
## GREECE 2.1222 [2.1208; 2.1237] 0.8 1.6 HIC
## HUNGARY 0.9152 [0.9142; 0.9162] 0.3 1.6 HIC
## INDIA 0.2189 [0.2188; 0.2189] 10.2 1.6 LMIC
## IRELAND 4.6540 [4.6507; 4.6573] 0.8 1.6 HIC
## ITALY 1.4848 [1.4843; 1.4853] 3.3 1.6 HIC
## JAPAN 1.0941 [1.0938; 1.0944] 5.2 1.6 HIC
## JORDAN 0.1713 [0.1709; 0.1718] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0219 [0.0218; 0.0220] 0.0 1.6 UMIC
## KUWAIT 0.3129 [0.3119; 0.3139] 0.0 1.6 HIC
## LATVIA 0.0739 [0.0733; 0.0745] 0.0 1.6 HIC
## LEBANON 0.4941 [0.4931; 0.4951] 0.1 1.6 UMIC
## LITHUANIA 0.5821 [0.5807; 0.5835] 0.1 1.6 HIC
## LUXEMBOURG 3.3108 [3.3027; 3.3189] 0.1 1.6 HIC
## MEXICO 0.1834 [0.1833; 0.1835] 0.8 1.6 UMIC
## MOROCCO 0.0481 [0.0479; 0.0482] 0.1 1.6 LMIC
## NETHERLANDS 1.9627 [1.9616; 1.9638] 1.2 1.6 HIC
## NEW ZEALAND 0.0595 [0.0591; 0.0599] 0.0 1.6 HIC
## NORWAY 2.5317 [2.5294; 2.5340] 0.5 1.6 HIC
## PAKISTAN 0.1655 [0.1654; 0.1656] 1.1 1.6 LMIC
## PERU 0.0764 [0.0762; 0.0766] 0.1 1.6 UMIC
## PHILIPPINES 0.0498 [0.0498; 0.0499] 0.2 1.6 LMIC
## POLAND 0.0124 [0.0123; 0.0124] 0.0 1.6 HIC
## PORTUGAL 3.1571 [3.1554; 3.1589] 1.2 1.6 HIC
## PUERTO RICO 1.2526 [1.2506; 1.2545] 0.2 1.6 HIC
## ROMANIA 0.2265 [0.2262; 0.2269] 0.2 1.6 UMIC
## RUSSIA 0.2953 [0.2951; 0.2954] 1.6 1.6 UMIC
## SAUDI ARABIA 0.6099 [0.6094; 0.6103] 0.7 1.6 HIC
## SERBIA 0.0514 [0.0512; 0.0517] 0.0 1.6 UMIC
## SLOVAKIA 2.1352 [2.1332; 2.1373] 0.4 1.6 HIC
## SLOVENIA 2.0907 [2.0875; 2.0940] 0.2 1.6 HIC
## SOUTH AFRICA 0.1847 [0.1845; 0.1849] 0.4 1.6 UMIC
## SOUTH KOREA 1.0076 [1.0072; 1.0081] 1.9 1.6 HIC
## SPAIN 3.8469 [3.8460; 3.8478] 6.7 1.6 HIC
## SWEDEN 3.1038 [3.1020; 3.1057] 1.1 1.6 HIC
## SWITZERLAND 2.2407 [2.2390; 2.2424] 0.7 1.6 HIC
## TUNISIA 0.2878 [0.2873; 0.2883] 0.1 1.6 LMIC
## TÜRKIYE 0.7664 [0.7661; 0.7668] 2.1 1.6 UMIC
## UNITED ARAB EMIRATES 0.2766 [0.2760; 0.2772] 0.1 1.6 HIC
## UNITED KINGDOM 3.3444 [3.3437; 3.3452] 8.0 1.6 HIC
## UNITED STATES 1.9036 [1.9033; 1.9038] 22.1 1.6 HIC
## URUGUAY 0.6758 [0.6743; 0.6772] 0.1 1.6 HIC
## VENEZUELA 0.4633 [0.4629; 0.4637] 0.5 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 1.4147 [1.4147; 1.4148] 10909.81 0
## Random effects model 0.4105 [0.3167; 0.5321] -6.73 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1038 [1.0357; 2.7740]; tau = 1.0506 [1.0177; 1.6655]
## I^2 = 100.0%; H = 4003.31
##
## Test of heterogeneity:
## Q d.f. p-value
## 993644337.60 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 2.2415 [2.2413; 2.2416] 159243317.19 100.0%
## income = UMIC 20 0.3578 [0.3577; 0.3579] 51540470.28 100.0%
## income = LMIC 6 0.2167 [0.2166; 0.2167] 8519755.50 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 774340794.63 2 0
## Within groups 219303542.96 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.0592 [0.9060; 1.2383] 0.2351 0.4848
## income = UMIC 20 0.0975 [0.0677; 0.1403] 0.6913 0.8314
## income = LMIC 6 0.1433 [0.1009; 0.2036] 0.1922 0.4384
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 210.13 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.6123 [0.6119; 0.6128] 0.7 1.6 UMIC
## ARGENTINA 0.8307 [0.8302; 0.8311] 1.1 1.6 UMIC
## AUSTRALIA 2.9087 [2.9076; 2.9099] 2.1 1.6 HIC
## AUSTRIA 2.0392 [2.0376; 2.0408] 0.6 1.6 HIC
## BELARUS 0.0122 [0.0121; 0.0124] 0.0 1.6 UMIC
## BELGIUM 2.0065 [2.0051; 2.0078] 0.7 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0126 [0.0124; 0.0128] 0.0 1.6 UMIC
## BRAZIL 0.1486 [0.1485; 0.1487] 0.9 1.6 UMIC
## BULGARIA 0.0796 [0.0793; 0.0799] 0.0 1.6 UMIC
## CANADA 3.6217 [3.6207; 3.6228] 4.0 1.6 HIC
## CHILE 0.4682 [0.4676; 0.4687] 0.3 1.6 HIC
## CHINA 0.0005 [0.0005; 0.0005] 0.0 1.6 UMIC
## COLOMBIA 0.0892 [0.0891; 0.0893] 0.1 1.6 UMIC
## CROATIA 0.3609 [0.3599; 0.3618] 0.0 1.6 HIC
## CZECH REPUBLIC 0.9899 [0.9889; 0.9909] 0.3 1.6 HIC
## ECUADOR 0.3316 [0.3311; 0.3320] 0.2 1.6 UMIC
## EGYPT 0.4690 [0.4688; 0.4693] 1.3 1.6 LMIC
## ESTONIA 0.3397 [0.3381; 0.3414] 0.0 1.6 HIC
## FINLAND 5.1196 [5.1165; 5.1228] 0.9 1.6 HIC
## FRANCE 3.5752 [3.5744; 3.5760] 7.2 1.6 HIC
## GERMANY 2.6646 [2.6640; 2.6652] 6.8 1.6 HIC
## GREECE 2.3245 [2.3230; 2.3260] 0.8 1.6 HIC
## HUNGARY 1.0646 [1.0635; 1.0657] 0.3 1.6 HIC
## INDIA 0.2404 [0.2404; 0.2405] 9.7 1.6 LMIC
## IRELAND 5.8241 [5.8205; 5.8277] 0.8 1.6 HIC
## ITALY 1.6107 [1.6102; 1.6112] 3.1 1.6 HIC
## JAPAN 1.4798 [1.4795; 1.4802] 6.0 1.6 HIC
## JORDAN 0.3112 [0.3106; 0.3118] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0284 [0.0283; 0.0285] 0.0 1.6 UMIC
## KUWAIT 0.6898 [0.6884; 0.6913] 0.1 1.6 HIC
## LATVIA 0.0910 [0.0904; 0.0917] 0.0 1.6 HIC
## LEBANON 0.5122 [0.5112; 0.5131] 0.1 1.6 UMIC
## LITHUANIA 0.7040 [0.7024; 0.7056] 0.1 1.6 HIC
## LUXEMBOURG 3.3751 [3.3670; 3.3832] 0.1 1.6 HIC
## MEXICO 0.1822 [0.1821; 0.1823] 0.7 1.6 UMIC
## MOROCCO 0.0512 [0.0510; 0.0513] 0.1 1.6 LMIC
## NETHERLANDS 2.1733 [2.1722; 2.1745] 1.2 1.6 HIC
## NEW ZEALAND 0.0746 [0.0742; 0.0751] 0.0 1.6 HIC
## NORWAY 2.5846 [2.5823; 2.5869] 0.4 1.6 HIC
## PAKISTAN 0.1842 [0.1841; 0.1843] 1.1 1.6 LMIC
## PERU 0.0920 [0.0918; 0.0922] 0.1 1.6 UMIC
## PHILIPPINES 0.0567 [0.0567; 0.0568] 0.2 1.6 LMIC
## POLAND 0.0158 [0.0158; 0.0159] 0.0 1.6 HIC
## PORTUGAL 3.1227 [3.1210; 3.1245] 1.0 1.6 HIC
## PUERTO RICO 1.2474 [1.2455; 1.2494] 0.1 1.6 HIC
## ROMANIA 0.1936 [0.1933; 0.1939] 0.1 1.6 UMIC
## RUSSIA 0.6942 [0.6940; 0.6945] 3.2 1.6 UMIC
## SAUDI ARABIA 0.9389 [0.9383; 0.9395] 0.9 1.6 HIC
## SERBIA 0.1583 [0.1578; 0.1587] 0.0 1.6 UMIC
## SLOVAKIA 2.2169 [2.2148; 2.2190] 0.4 1.6 HIC
## SLOVENIA 2.2580 [2.2546; 2.2614] 0.1 1.6 HIC
## SOUTH AFRICA 0.1957 [0.1955; 0.1959] 0.3 1.6 UMIC
## SOUTH KOREA 1.1367 [1.1362; 1.1372] 1.8 1.6 HIC
## SPAIN 4.1742 [4.1732; 4.1751] 6.2 1.6 HIC
## SWEDEN 3.1872 [3.1853; 3.1890] 1.0 1.6 HIC
## SWITZERLAND 2.4526 [2.4508; 2.4544] 0.6 1.6 HIC
## TUNISIA 0.3460 [0.3454; 0.3466] 0.1 1.6 LMIC
## TÜRKIYE 1.0245 [1.0241; 1.0248] 2.5 1.6 UMIC
## UNITED ARAB EMIRATES 0.4835 [0.4828; 0.4843] 0.1 1.6 HIC
## UNITED KINGDOM 4.0399 [4.0391; 4.0407] 8.3 1.6 HIC
## UNITED STATES 2.0294 [2.0291; 2.0297] 20.3 1.6 HIC
## URUGUAY 0.7672 [0.7657; 0.7688] 0.1 1.6 HIC
## VENEZUELA 0.6107 [0.6102; 0.6111] 0.6 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 1.5881 [1.5880; 1.5882] 15737.80 0
## Random effects model 0.5122 [0.3987; 0.6582] -5.23 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0306 [0.9787; 2.5907]; tau = 1.0152 [0.9893; 1.6096]
## I^2 = 100.0%; H = 4205.32
##
## Test of heterogeneity:
## Q d.f. p-value
## 1096453946.47 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 2.5113 [2.5112; 2.5115] 166828306.04 100.0%
## income = UMIC 20 0.5276 [0.5276; 0.5277] 75022370.69 100.0%
## income = LMIC 6 0.2459 [0.2459; 0.2459] 13566525.34 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 841036744.40 2 0
## Within groups 255417202.07 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.2766 [1.1002; 1.4812] 0.2128 0.4613
## income = UMIC 20 0.1327 [0.0914; 0.1926] 0.7237 0.8507
## income = LMIC 6 0.1659 [0.1112; 0.2475] 0.2500 0.5000
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 186.42 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.8746 [0.8741; 0.8750] 0.9 1.6 UMIC
## ARGENTINA 0.9517 [0.9512; 0.9522] 1.1 1.6 UMIC
## AUSTRALIA 5.2042 [5.2027; 5.2057] 3.4 1.6 HIC
## AUSTRIA 2.2212 [2.2196; 2.2229] 0.5 1.6 HIC
## BELARUS 0.0145 [0.0144; 0.0146] 0.0 1.6 UMIC
## BELGIUM 2.0464 [2.0450; 2.0478] 0.6 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0180 [0.0177; 0.0182] 0.0 1.6 UMIC
## BRAZIL 0.1909 [0.1908; 0.1910] 1.1 1.6 UMIC
## BULGARIA 0.1036 [0.1032; 0.1040] 0.0 1.6 UMIC
## CANADA 4.1094 [4.1083; 4.1105] 4.0 1.6 HIC
## CHILE 0.5657 [0.5651; 0.5663] 0.3 1.6 HIC
## CHINA 0.0006 [0.0006; 0.0006] 0.0 1.6 UMIC
## COLOMBIA 0.1081 [0.1080; 0.1083] 0.1 1.6 UMIC
## CROATIA 0.4212 [0.4202; 0.4222] 0.0 1.6 HIC
## CZECH REPUBLIC 1.1031 [1.1020; 1.1041] 0.3 1.6 HIC
## ECUADOR 0.3778 [0.3774; 0.3783] 0.2 1.6 UMIC
## EGYPT 0.6020 [0.6018; 0.6023] 1.5 1.6 LMIC
## ESTONIA 0.4430 [0.4412; 0.4449] 0.0 1.6 HIC
## FINLAND 4.6536 [4.6506; 4.6566] 0.7 1.6 HIC
## FRANCE 3.8924 [3.8916; 3.8932] 6.8 1.6 HIC
## GERMANY 2.8672 [2.8666; 2.8678] 6.4 1.6 HIC
## GREECE 2.3194 [2.3179; 2.3209] 0.7 1.6 HIC
## HUNGARY 1.1741 [1.1730; 1.1752] 0.3 1.6 HIC
## INDIA 0.2673 [0.2672; 0.2673] 9.5 1.6 LMIC
## IRELAND 6.3788 [6.3750; 6.3826] 0.8 1.6 HIC
## ITALY 1.7371 [1.7366; 1.7377] 2.9 1.6 HIC
## JAPAN 1.9536 [1.9532; 1.9540] 6.9 1.6 HIC
## JORDAN 0.4901 [0.4893; 0.4909] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0394 [0.0393; 0.0396] 0.0 1.6 UMIC
## KUWAIT 0.5753 [0.5740; 0.5766] 0.1 1.6 HIC
## LATVIA 0.0989 [0.0982; 0.0996] 0.0 1.6 HIC
## LEBANON 0.5807 [0.5797; 0.5817] 0.1 1.6 UMIC
## LITHUANIA 0.8657 [0.8639; 0.8674] 0.1 1.6 HIC
## LUXEMBOURG 3.5380 [3.5298; 3.5462] 0.1 1.6 HIC
## MEXICO 0.2060 [0.2059; 0.2062] 0.7 1.6 UMIC
## MOROCCO 0.0548 [0.0547; 0.0550] 0.1 1.6 LMIC
## NETHERLANDS 2.4586 [2.4574; 2.4599] 1.1 1.6 HIC
## NEW ZEALAND 0.0862 [0.0858; 0.0867] 0.0 1.6 HIC
## NORWAY 2.7973 [2.7949; 2.7997] 0.4 1.6 HIC
## PAKISTAN 0.2062 [0.2061; 0.2063] 1.1 1.6 LMIC
## PERU 0.1047 [0.1045; 0.1049] 0.1 1.6 UMIC
## PHILIPPINES 0.0660 [0.0659; 0.0661] 0.2 1.6 LMIC
## POLAND 0.0386 [0.0385; 0.0387] 0.0 1.6 HIC
## PORTUGAL 3.6046 [3.6027; 3.6065] 1.0 1.6 HIC
## PUERTO RICO 1.2377 [1.2358; 1.2396] 0.1 1.6 HIC
## ROMANIA 0.1583 [0.1580; 0.1585] 0.1 1.6 UMIC
## RUSSIA 0.8927 [0.8925; 0.8930] 3.5 1.6 UMIC
## SAUDI ARABIA 1.2680 [1.2673; 1.2687] 1.1 1.6 HIC
## SERBIA 0.3061 [0.3055; 0.3067] 0.1 1.6 UMIC
## SLOVAKIA 2.2800 [2.2779; 2.2821] 0.3 1.6 HIC
## SLOVENIA 2.6181 [2.6145; 2.6218] 0.1 1.6 HIC
## SOUTH AFRICA 0.2145 [0.2143; 0.2147] 0.3 1.6 UMIC
## SOUTH KOREA 1.2171 [1.2166; 1.2176] 1.7 1.6 HIC
## SPAIN 4.4074 [4.4064; 4.4084] 5.6 1.6 HIC
## SWEDEN 3.1711 [3.1693; 3.1730] 0.8 1.6 HIC
## SWITZERLAND 2.6923 [2.6904; 2.6941] 0.6 1.6 HIC
## TUNISIA 0.3867 [0.3861; 0.3873] 0.1 1.6 LMIC
## TÜRKIYE 1.5111 [1.5106; 1.5115] 3.2 1.6 UMIC
## UNITED ARAB EMIRATES 0.7207 [0.7198; 0.7216] 0.2 1.6 HIC
## UNITED KINGDOM 4.8630 [4.8621; 4.8638] 8.7 1.6 HIC
## UNITED STATES 2.1150 [2.1147; 2.1153] 18.4 1.6 HIC
## URUGUAY 0.9316 [0.9299; 0.9333] 0.1 1.6 HIC
## VENEZUELA 0.7561 [0.7556; 0.7566] 0.6 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 1.7954 [1.7953; 1.7955] 21380.82 0
## Random effects model 0.6048 [0.4718; 0.7754] -3.97 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0118 [0.9529; 2.5093]; tau = 1.0059 [0.9761; 1.5841]
## I^2 = 100.0%; H = 4488.94
##
## Test of heterogeneity:
## Q d.f. p-value
## 1249336268.40 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 2.8317 [2.8316; 2.8319] 205087332.47 100.0%
## income = UMIC 20 0.7163 [0.7162; 0.7164] 107183852.64 100.0%
## income = LMIC 6 0.2812 [0.2812; 0.2813] 20345568.29 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 916719514.99 2 0
## Within groups 332616753.41 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.4680 [1.2580; 1.7131] 0.2295 0.4791
## income = UMIC 20 0.1661 [0.1124; 0.2456] 0.7957 0.8920
## income = LMIC 6 0.1896 [0.1214; 0.2961] 0.3107 0.5574
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 157.02 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.0163 [1.0158; 1.0168] 1.0 1.6 UMIC
## ARGENTINA 1.1057 [1.1052; 1.1062] 1.2 1.6 UMIC
## AUSTRALIA 6.5685 [6.5668; 6.5702] 3.9 1.6 HIC
## AUSTRIA 2.3459 [2.3443; 2.3476] 0.5 1.6 HIC
## BELARUS 0.0132 [0.0131; 0.0133] 0.0 1.6 UMIC
## BELGIUM 2.3114 [2.3100; 2.3129] 0.6 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0204 [0.0202; 0.0207] 0.0 1.6 UMIC
## BRAZIL 0.2552 [0.2551; 0.2553] 1.3 1.6 UMIC
## BULGARIA 0.1436 [0.1432; 0.1441] 0.0 1.6 UMIC
## CANADA 4.6887 [4.6875; 4.6899] 4.2 1.6 HIC
## CHILE 0.6778 [0.6772; 0.6785] 0.3 1.6 HIC
## CHINA 0.0009 [0.0009; 0.0009] 0.0 1.6 UMIC
## COLOMBIA 0.1500 [0.1498; 0.1502] 0.2 1.6 UMIC
## CROATIA 0.5326 [0.5314; 0.5337] 0.1 1.6 HIC
## CZECH REPUBLIC 1.4366 [1.4354; 1.4378] 0.4 1.6 HIC
## ECUADOR 0.4372 [0.4367; 0.4377] 0.2 1.6 UMIC
## EGYPT 0.8155 [0.8152; 0.8158] 1.9 1.6 LMIC
## ESTONIA 0.6044 [0.6022; 0.6066] 0.0 1.6 HIC
## FINLAND 4.5624 [4.5594; 4.5654] 0.6 1.6 HIC
## FRANCE 4.0923 [4.0915; 4.0931] 6.5 1.6 HIC
## GERMANY 3.1020 [3.1013; 3.1026] 6.3 1.6 HIC
## GREECE 2.3751 [2.3736; 2.3766] 0.6 1.6 HIC
## HUNGARY 1.1020 [1.1009; 1.1031] 0.3 1.6 HIC
## INDIA 0.3002 [0.3002; 0.3003] 9.7 1.6 LMIC
## IRELAND 8.0270 [8.0227; 8.0312] 0.9 1.6 HIC
## ITALY 1.8267 [1.8261; 1.8273] 2.7 1.6 HIC
## JAPAN 2.0991 [2.0987; 2.0995] 6.7 1.6 HIC
## JORDAN 0.5068 [0.5060; 0.5075] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0626 [0.0624; 0.0628] 0.0 1.6 UMIC
## KUWAIT 0.6329 [0.6316; 0.6342] 0.1 1.6 HIC
## LATVIA 0.0847 [0.0840; 0.0854] 0.0 1.6 HIC
## LEBANON 0.6932 [0.6922; 0.6943] 0.1 1.6 UMIC
## LITHUANIA 0.9018 [0.9000; 0.9036] 0.1 1.6 HIC
## LUXEMBOURG 3.4906 [3.4825; 3.4986] 0.0 1.6 HIC
## MEXICO 0.2250 [0.2249; 0.2252] 0.7 1.6 UMIC
## MOROCCO 0.0612 [0.0611; 0.0613] 0.1 1.6 LMIC
## NETHERLANDS 2.6687 [2.6674; 2.6700] 1.1 1.6 HIC
## NEW ZEALAND 0.0988 [0.0984; 0.0993] 0.0 1.6 HIC
## NORWAY 2.9604 [2.9579; 2.9628] 0.4 1.6 HIC
## PAKISTAN 0.2419 [0.2418; 0.2420] 1.2 1.6 LMIC
## PERU 0.1435 [0.1433; 0.1437] 0.1 1.6 UMIC
## PHILIPPINES 0.0760 [0.0759; 0.0761] 0.2 1.6 LMIC
## POLAND 0.0663 [0.0662; 0.0665] 0.1 1.6 HIC
## PORTUGAL 3.7457 [3.7438; 3.7477] 1.0 1.6 HIC
## PUERTO RICO 1.1744 [1.1725; 1.1763] 0.1 1.6 HIC
## ROMANIA 0.1620 [0.1617; 0.1622] 0.1 1.6 UMIC
## RUSSIA 0.7880 [0.7878; 0.7882] 2.8 1.6 UMIC
## SAUDI ARABIA 1.0275 [1.0269; 1.0281] 0.8 1.6 HIC
## SERBIA 0.6683 [0.6674; 0.6692] 0.1 1.6 UMIC
## SLOVAKIA 2.5188 [2.5166; 2.5210] 0.3 1.6 HIC
## SLOVENIA 2.9286 [2.9248; 2.9325] 0.2 1.6 HIC
## SOUTH AFRICA 0.2358 [0.2356; 0.2360] 0.3 1.6 UMIC
## SOUTH KOREA 1.3430 [1.3425; 1.3435] 1.7 1.6 HIC
## SPAIN 4.4202 [4.4192; 4.4212] 5.1 1.6 HIC
## SWEDEN 3.1973 [3.1954; 3.1991] 0.8 1.6 HIC
## SWITZERLAND 2.9807 [2.9788; 2.9826] 0.6 1.6 HIC
## TUNISIA 0.4564 [0.4558; 0.4571] 0.1 1.6 LMIC
## TÜRKIYE 2.0684 [2.0679; 2.0689] 4.0 1.6 UMIC
## UNITED ARAB EMIRATES 0.9940 [0.9929; 0.9951] 0.2 1.6 HIC
## UNITED KINGDOM 5.6856 [5.6847; 5.6866] 9.3 1.6 HIC
## UNITED STATES 2.1958 [2.1955; 2.1960] 17.4 1.6 HIC
## URUGUAY 1.1259 [1.1241; 1.1278] 0.1 1.6 HIC
## VENEZUELA 0.7361 [0.7356; 0.7366] 0.5 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 1.9491 [1.9490; 1.9492] 25631.63 0
## Random effects model 0.6941 [0.5394; 0.8932] -2.84 0.0046
##
## Quantifying heterogeneity:
## tau^2 = 1.0435 [0.9465; 2.4751]; tau = 1.0215 [0.9729; 1.5732]
## I^2 = 100.0%; H = 4799.70
##
## Test of heterogeneity:
## Q d.f. p-value
## 1428302696.42 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 3.1005 [3.1003; 3.1007] 264246386.38 100.0%
## income = UMIC 20 0.8292 [0.8291; 0.8294] 146881974.66 100.0%
## income = LMIC 6 0.3317 [0.3317; 0.3317] 34021718.52 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 983152616.87 2 0
## Within groups 445150079.56 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.6198 [1.3694; 1.9160] 0.2717 0.5212
## income = UMIC 20 0.2032 [0.1329; 0.3108] 0.9387 0.9689
## income = LMIC 6 0.2238 [0.1339; 0.3741] 0.4124 0.6422
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 117.80 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.3989 [1.3983; 1.3995] 1.3 1.6 UMIC
## ARGENTINA 1.1449 [1.1443; 1.1454] 1.1 1.6 UMIC
## AUSTRALIA 7.7014 [7.6996; 7.7032] 4.2 1.6 HIC
## AUSTRIA 3.3197 [3.3177; 3.3217] 0.7 1.6 HIC
## BELARUS 0.0210 [0.0208; 0.0211] 0.0 1.6 UMIC
## BELGIUM 3.5944 [3.5926; 3.5963] 0.9 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0389 [0.0386; 0.0393] 0.0 1.6 UMIC
## BRAZIL 0.3278 [0.3277; 0.3280] 1.5 1.6 UMIC
## BULGARIA 0.4194 [0.4186; 0.4202] 0.1 1.6 UMIC
## CANADA 5.2954 [5.2942; 5.2966] 4.4 1.6 HIC
## CHILE 0.7712 [0.7705; 0.7719] 0.3 1.6 HIC
## CHINA 0.0011 [0.0011; 0.0011] 0.0 1.6 UMIC
## COLOMBIA 0.1520 [0.1518; 0.1521] 0.2 1.6 UMIC
## CROATIA 0.8785 [0.8770; 0.8800] 0.1 1.6 HIC
## CZECH REPUBLIC 2.2297 [2.2282; 2.2312] 0.5 1.6 HIC
## ECUADOR 0.4496 [0.4491; 0.4501] 0.2 1.6 UMIC
## EGYPT 1.1842 [1.1839; 1.1846] 2.5 1.6 LMIC
## ESTONIA 0.8538 [0.8512; 0.8564] 0.0 1.6 HIC
## FINLAND 4.7260 [4.7230; 4.7290] 0.6 1.6 HIC
## FRANCE 4.1885 [4.1877; 4.1893] 6.2 1.6 HIC
## GERMANY 3.3860 [3.3854; 3.3867] 6.3 1.6 HIC
## GREECE 2.9066 [2.9049; 2.9083] 0.7 1.6 HIC
## HUNGARY 1.1824 [1.1813; 1.1836] 0.3 1.6 HIC
## INDIA 0.3294 [0.3293; 0.3294] 9.9 1.6 LMIC
## IRELAND 8.6626 [8.6582; 8.6670] 0.9 1.6 HIC
## ITALY 1.8764 [1.8759; 1.8770] 2.6 1.6 HIC
## JAPAN 2.2892 [2.2887; 2.2896] 6.6 1.6 HIC
## JORDAN 0.8144 [0.8134; 0.8153] 0.2 1.6 UMIC
## KAZAKHSTAN 0.0466 [0.0464; 0.0467] 0.0 1.6 UMIC
## KUWAIT 0.8609 [0.8594; 0.8624] 0.1 1.6 HIC
## LATVIA 0.1226 [0.1218; 0.1234] 0.0 1.6 HIC
## LEBANON 0.8859 [0.8847; 0.8870] 0.1 1.6 UMIC
## LITHUANIA 1.2160 [1.2139; 1.2181] 0.1 1.6 HIC
## LUXEMBOURG 3.6592 [3.6511; 3.6674] 0.0 1.6 HIC
## MEXICO 0.2381 [0.2379; 0.2382] 0.7 1.6 UMIC
## MOROCCO 0.0617 [0.0615; 0.0618] 0.0 1.6 LMIC
## NETHERLANDS 2.8536 [2.8523; 2.8549] 1.1 1.6 HIC
## NEW ZEALAND 0.1128 [0.1123; 0.1133] 0.0 1.6 HIC
## NORWAY 3.0757 [3.0733; 3.0782] 0.4 1.6 HIC
## PAKISTAN 0.2771 [0.2770; 0.2773] 1.3 1.6 LMIC
## PERU 0.1565 [0.1563; 0.1567] 0.1 1.6 UMIC
## PHILIPPINES 0.0904 [0.0904; 0.0905] 0.2 1.6 LMIC
## POLAND 0.3295 [0.3292; 0.3298] 0.3 1.6 HIC
## PORTUGAL 3.5641 [3.5622; 3.5660] 0.8 1.6 HIC
## PUERTO RICO 1.1502 [1.1483; 1.1521] 0.1 1.6 HIC
## ROMANIA 0.1850 [0.1847; 0.1853] 0.1 1.6 UMIC
## RUSSIA 0.1759 [0.1758; 0.1760] 0.6 1.6 UMIC
## SAUDI ARABIA 0.8962 [0.8957; 0.8968] 0.7 1.6 HIC
## SERBIA 1.0159 [1.0148; 1.0170] 0.2 1.6 UMIC
## SLOVAKIA 3.5201 [3.5175; 3.5227] 0.4 1.6 HIC
## SLOVENIA 3.3094 [3.3053; 3.3135] 0.2 1.6 HIC
## SOUTH AFRICA 0.2596 [0.2594; 0.2599] 0.3 1.6 UMIC
## SOUTH KOREA 1.5523 [1.5517; 1.5529] 1.8 1.6 HIC
## SPAIN 4.6644 [4.6634; 4.6655] 4.9 1.6 HIC
## SWEDEN 3.1920 [3.1902; 3.1939] 0.7 1.6 HIC
## SWITZERLAND 3.2221 [3.2201; 3.2242] 0.6 1.6 HIC
## TUNISIA 0.5180 [0.5173; 0.5187] 0.1 1.6 LMIC
## TÜRKIYE 2.5201 [2.5195; 2.5207] 4.6 1.6 UMIC
## UNITED ARAB EMIRATES 0.7037 [0.7028; 0.7046] 0.1 1.6 HIC
## UNITED KINGDOM 6.5755 [6.5745; 6.5766] 9.9 1.6 HIC
## UNITED STATES 2.2399 [2.2396; 2.2401] 16.4 1.6 HIC
## URUGUAY 1.1942 [1.1923; 1.1961] 0.1 1.6 HIC
## VENEZUELA 0.5555 [0.5550; 0.5559] 0.4 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 2.1551 [2.1550; 2.1552] 30779.54 0
## Random effects model 0.8125 [0.6278; 1.0515] -1.58 0.1145
##
## Quantifying heterogeneity:
## tau^2 = 1.0903 [0.9721; 2.5199]; tau = 1.0442 [0.9860; 1.5874]
## I^2 = 100.0%; H = 5125.32
##
## Test of heterogeneity:
## Q d.f. p-value
## 1628674100.14 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 3.3869 [3.3867; 3.3871] 331927424.71 100.0%
## income = UMIC 20 0.9364 [0.9363; 0.9365] 210501886.72 100.0%
## income = LMIC 6 0.3997 [0.3996; 0.3997] 67393950.79 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1018850837.92 2 0
## Within groups 609823262.21 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.9236 [1.6074; 2.3019] 0.3105 0.5572
## income = UMIC 20 0.2321 [0.1380; 0.3903] 1.4069 1.1861
## income = LMIC 6 0.2605 [0.1377; 0.4929] 0.6352 0.7970
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 84.56 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 2.9210 [2.9202; 2.9219] 2.4 1.6 UMIC
## ARGENTINA 1.2095 [1.2090; 1.2101] 1.1 1.6 UMIC
## AUSTRALIA 8.3465 [8.3446; 8.3484] 4.2 1.6 HIC
## AUSTRIA 3.5100 [3.5080; 3.5121] 0.6 1.6 HIC
## BELARUS 0.0380 [0.0377; 0.0382] 0.0 1.6 UMIC
## BELGIUM 4.2151 [4.2131; 4.2170] 1.0 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0747 [0.0742; 0.0752] 0.0 1.6 UMIC
## BRAZIL 0.4008 [0.4007; 0.4010] 1.7 1.6 UMIC
## BULGARIA 0.6915 [0.6905; 0.6925] 0.1 1.6 UMIC
## CANADA 5.8582 [5.8569; 5.8594] 4.4 1.6 HIC
## CHILE 0.8596 [0.8589; 0.8603] 0.3 1.6 HIC
## CHINA 0.0019 [0.0019; 0.0019] 0.1 1.6 UMIC
## COLOMBIA 0.1610 [0.1608; 0.1612] 0.2 1.6 UMIC
## CROATIA 1.0545 [1.0529; 1.0561] 0.1 1.6 HIC
## CZECH REPUBLIC 2.6821 [2.6804; 2.6837] 0.6 1.6 HIC
## ECUADOR 0.4751 [0.4746; 0.4757] 0.2 1.6 UMIC
## EGYPT 1.9074 [1.9069; 1.9078] 3.7 1.6 LMIC
## ESTONIA 1.1673 [1.1643; 1.1704] 0.0 1.6 HIC
## FINLAND 4.8979 [4.8948; 4.9009] 0.5 1.6 HIC
## FRANCE 4.2756 [4.2747; 4.2764] 5.6 1.6 HIC
## GERMANY 3.5403 [3.5397; 3.5410] 5.9 1.6 HIC
## GREECE 3.2721 [3.2703; 3.2740] 0.7 1.6 HIC
## HUNGARY 1.2680 [1.2668; 1.2692] 0.2 1.6 HIC
## INDIA 0.3586 [0.3586; 0.3587] 9.7 1.6 LMIC
## IRELAND 8.7396 [8.7352; 8.7440] 0.8 1.6 HIC
## ITALY 2.0769 [2.0763; 2.0775] 2.6 1.6 HIC
## JAPAN 2.5718 [2.5714; 2.5723] 6.6 1.6 HIC
## JORDAN 1.0108 [1.0097; 1.0118] 0.2 1.6 UMIC
## KAZAKHSTAN 0.0649 [0.0648; 0.0651] 0.0 1.6 UMIC
## KUWAIT 1.7104 [1.7083; 1.7125] 0.1 1.6 HIC
## LATVIA 0.1880 [0.1870; 0.1890] 0.0 1.6 HIC
## LEBANON 0.9871 [0.9858; 0.9883] 0.1 1.6 UMIC
## LITHUANIA 1.1781 [1.1760; 1.1802] 0.1 1.6 HIC
## LUXEMBOURG 3.8108 [3.8026; 3.8190] 0.0 1.6 HIC
## MEXICO 0.2819 [0.2817; 0.2820] 0.7 1.6 UMIC
## MOROCCO 0.1094 [0.1092; 0.1095] 0.1 1.6 LMIC
## NETHERLANDS 3.0097 [3.0083; 3.0110] 1.0 1.6 HIC
## NEW ZEALAND 0.1245 [0.1240; 0.1250] 0.0 1.6 HIC
## NORWAY 3.3128 [3.3103; 3.3154] 0.4 1.6 HIC
## PAKISTAN 0.3093 [0.3092; 0.3094] 1.3 1.6 LMIC
## PERU 0.1504 [0.1501; 0.1506] 0.1 1.6 UMIC
## PHILIPPINES 0.1034 [0.1033; 0.1035] 0.2 1.6 LMIC
## POLAND 0.7188 [0.7183; 0.7192] 0.6 1.6 HIC
## PORTUGAL 3.5659 [3.5640; 3.5678] 0.7 1.6 HIC
## PUERTO RICO 1.1639 [1.1619; 1.1658] 0.1 1.6 HIC
## ROMANIA 0.2287 [0.2284; 0.2291] 0.1 1.6 UMIC
## RUSSIA 0.2144 [0.2142; 0.2145] 0.6 1.6 UMIC
## SAUDI ARABIA 1.3068 [1.3061; 1.3074] 0.9 1.6 HIC
## SERBIA 1.3730 [1.3717; 1.3743] 0.2 1.6 UMIC
## SLOVAKIA 3.1333 [3.1309; 3.1358] 0.3 1.6 HIC
## SLOVENIA 3.4915 [3.4873; 3.4957] 0.1 1.6 HIC
## SOUTH AFRICA 0.2695 [0.2693; 0.2697] 0.3 1.6 UMIC
## SOUTH KOREA 1.8268 [1.8262; 1.8274] 1.9 1.6 HIC
## SPAIN 4.8410 [4.8400; 4.8421] 4.6 1.6 HIC
## SWEDEN 3.1877 [3.1859; 3.1896] 0.6 1.6 HIC
## SWITZERLAND 3.3115 [3.3095; 3.3136] 0.6 1.6 HIC
## TUNISIA 0.7289 [0.7281; 0.7297] 0.2 1.6 LMIC
## TÜRKIYE 3.2653 [3.2646; 3.2659] 5.4 1.6 UMIC
## UNITED ARAB EMIRATES 0.6187 [0.6179; 0.6196] 0.1 1.6 HIC
## UNITED KINGDOM 7.2109 [7.2099; 7.2120] 9.7 1.6 HIC
## UNITED STATES 2.2482 [2.2479; 2.2485] 14.8 1.6 HIC
## URUGUAY 1.3737 [1.3716; 1.3757] 0.1 1.6 HIC
## VENEZUELA 0.3986 [0.3983; 0.3990] 0.2 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 2.3407 [2.3406; 2.3408] 36119.09 0
## Random effects model 0.9681 [0.7520; 1.2464] -0.25 0.8015
##
## Quantifying heterogeneity:
## tau^2 = 1.0466 [0.9322; 2.3928]; tau = 1.0230 [0.9655; 1.5469]
## I^2 = 100.0%; H = 5335.84
##
## Test of heterogeneity:
## Q d.f. p-value
## 1765213787.98 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 3.5932 [3.5930; 3.5934] 362890784.92 100.0%
## income = UMIC 20 1.3385 [1.3384; 1.3387] 317286197.81 100.0%
## income = LMIC 6 0.5245 [0.5244; 0.5246] 158180915.97 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 926855889.28 2 0
## Within groups 838357898.70 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 2.1718 [1.8128; 2.6018] 0.3143 0.5606
## income = UMIC 20 0.2955 [0.1690; 0.5164] 1.6236 1.2742
## income = LMIC 6 0.3470 [0.1499; 0.8029] 1.0994 1.0485
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 58.55 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 2.8536 [2.8528; 2.8544] 2.2 1.6 UMIC
## ARGENTINA 1.2643 [1.2638; 1.2649] 1.0 1.6 UMIC
## AUSTRALIA 8.4291 [8.4272; 8.4310] 3.9 1.6 HIC
## AUSTRIA 3.8288 [3.8266; 3.8309] 0.6 1.6 HIC
## BELARUS 0.0642 [0.0639; 0.0645] 0.0 1.6 UMIC
## BELGIUM 4.5974 [4.5954; 4.5995] 1.0 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.1682 [0.1675; 0.1690] 0.0 1.6 UMIC
## BRAZIL 0.5116 [0.5115; 0.5118] 2.0 1.6 UMIC
## BULGARIA 0.9371 [0.9359; 0.9383] 0.1 1.6 UMIC
## CANADA 5.8958 [5.8945; 5.8971] 4.0 1.6 HIC
## CHILE 0.9066 [0.9059; 0.9073] 0.3 1.6 HIC
## CHINA 0.0022 [0.0022; 0.0022] 0.1 1.6 UMIC
## COLOMBIA 0.1810 [0.1808; 0.1812] 0.2 1.6 UMIC
## CROATIA 1.2204 [1.2186; 1.2221] 0.1 1.6 HIC
## CZECH REPUBLIC 3.0335 [3.0318; 3.0352] 0.6 1.6 HIC
## ECUADOR 0.4963 [0.4958; 0.4969] 0.2 1.6 UMIC
## EGYPT 3.7897 [3.7891; 3.7904] 6.9 1.6 LMIC
## ESTONIA 1.7170 [1.7133; 1.7207] 0.0 1.6 HIC
## FINLAND 5.0209 [5.0178; 5.0240] 0.5 1.6 HIC
## FRANCE 4.5106 [4.5098; 4.5115] 5.4 1.6 HIC
## GERMANY 3.7680 [3.7673; 3.7687] 5.8 1.6 HIC
## GREECE 3.6452 [3.6433; 3.6471] 0.7 1.6 HIC
## HUNGARY 1.3354 [1.3342; 1.3366] 0.2 1.6 HIC
## INDIA 0.4017 [0.4017; 0.4018] 10.0 1.6 LMIC
## IRELAND 7.0693 [7.0654; 7.0732] 0.6 1.6 HIC
## ITALY 2.1269 [2.1263; 2.1276] 2.4 1.6 HIC
## JAPAN 2.7661 [2.7657; 2.7666] 6.5 1.6 HIC
## JORDAN 0.3184 [0.3178; 0.3190] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0771 [0.0769; 0.0773] 0.0 1.6 UMIC
## KUWAIT 2.3085 [2.3061; 2.3110] 0.2 1.6 HIC
## LATVIA 0.2739 [0.2727; 0.2751] 0.0 1.6 HIC
## LEBANON 1.0700 [1.0688; 1.0713] 0.1 1.6 UMIC
## LITHUANIA 1.4094 [1.4071; 1.4118] 0.1 1.6 HIC
## LUXEMBOURG 3.7805 [3.7724; 3.7886] 0.0 1.6 HIC
## MEXICO 0.3123 [0.3121; 0.3124] 0.7 1.6 UMIC
## MOROCCO 0.1609 [0.1607; 0.1611] 0.1 1.6 LMIC
## NETHERLANDS 3.1648 [3.1634; 3.1662] 1.0 1.6 HIC
## NEW ZEALAND 1.0211 [1.0196; 1.0226] 0.1 1.6 HIC
## NORWAY 3.3764 [3.3738; 3.3790] 0.3 1.6 HIC
## PAKISTAN 0.3665 [0.3664; 0.3667] 1.4 1.6 LMIC
## PERU 0.1859 [0.1856; 0.1861] 0.1 1.6 UMIC
## PHILIPPINES 0.1243 [0.1242; 0.1245] 0.2 1.6 LMIC
## POLAND 1.1450 [1.1444; 1.1455] 0.8 1.6 HIC
## PORTUGAL 3.6571 [3.6551; 3.6590] 0.7 1.6 HIC
## PUERTO RICO 1.1902 [1.1882; 1.1923] 0.1 1.6 HIC
## ROMANIA 0.2424 [0.2420; 0.2427] 0.1 1.6 UMIC
## RUSSIA 0.2850 [0.2848; 0.2851] 0.8 1.6 UMIC
## SAUDI ARABIA 0.3547 [0.3544; 0.3550] 0.2 1.6 HIC
## SERBIA 1.8492 [1.8478; 1.8507] 0.3 1.6 UMIC
## SLOVAKIA 3.4195 [3.4169; 3.4220] 0.3 1.6 HIC
## SLOVENIA 3.6286 [3.6243; 3.6329] 0.1 1.6 HIC
## SOUTH AFRICA 0.3076 [0.3074; 0.3079] 0.3 1.6 UMIC
## SOUTH KOREA 2.1687 [2.1681; 2.1694] 2.0 1.6 HIC
## SPAIN 5.0047 [5.0036; 5.0057] 4.3 1.6 HIC
## SWEDEN 3.1316 [3.1297; 3.1334] 0.6 1.6 HIC
## SWITZERLAND 3.4301 [3.4281; 3.4322] 0.5 1.6 HIC
## TUNISIA 1.9236 [1.9223; 1.9250] 0.4 1.6 LMIC
## TÜRKIYE 3.5722 [3.5716; 3.5729] 5.4 1.6 UMIC
## UNITED ARAB EMIRATES 0.6074 [0.6066; 0.6082] 0.1 1.6 HIC
## UNITED KINGDOM 7.5390 [7.5379; 7.5401] 9.3 1.6 HIC
## UNITED STATES 2.2628 [2.2625; 2.2630] 13.6 1.6 HIC
## URUGUAY 1.2352 [1.2333; 1.2372] 0.1 1.6 HIC
## VENEZUELA 0.3925 [0.3921; 0.3928] 0.2 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 2.4887 [2.4886; 2.4888] 40643.71 0
## Random effects model 1.1096 [0.8666; 1.4206] 0.82 0.4097
##
## Quantifying heterogeneity:
## tau^2 = 1.0017 [0.8902; 2.2803]; tau = 1.0008 [0.9435; 1.5101]
## I^2 = 100.0%; H = 5484.40
##
## Test of heterogeneity:
## Q d.f. p-value
## 1864872910.92 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 3.7308 [3.7306; 3.7310] 368340329.39 100.0%
## income = UMIC 20 1.4026 [1.4025; 1.4028] 338964612.26 100.0%
## income = LMIC 6 0.9076 [0.9075; 0.9077] 463558183.03 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 694009786.24 2 0
## Within groups 1170863124.67 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 2.3929 [2.0021; 2.8601] 0.3063 0.5535
## income = UMIC 20 0.3347 [0.1937; 0.5782] 1.5562 1.2475
## income = LMIC 6 0.5272 [0.1662; 1.6729] 2.0825 1.4431
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 49.89 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0709 [0.0708; 0.0711] 0.1 1.7 UMIC
## ARGENTINA 0.2110 [0.2108; 0.2113] 0.2 1.7 UMIC
## AUSTRALIA 0.8473 [0.8467; 0.8480] 0.5 1.7 HIC
## AUSTRIA 2.0535 [2.0519; 2.0551] 0.5 1.7 HIC
## BELARUS 0.0050 [0.0049; 0.0051] 0.0 1.7 UMIC
## BELGIUM 1.2565 [1.2554; 1.2576] 0.4 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0522 [0.0522; 0.0523] 0.3 1.7 UMIC
## BULGARIA 0.0991 [0.0987; 0.0994] 0.0 1.7 UMIC
## CANADA 3.9297 [3.9286; 3.9308] 3.8 1.7 HIC
## CHILE 0.2113 [0.2110; 0.2117] 0.1 1.7 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0876 [0.0874; 0.0877] 0.1 1.7 UMIC
## CROATIA 0.3042 [0.3033; 0.3050] 0.0 1.7 HIC
## CZECH REPUBLIC 0.7856 [0.7847; 0.7865] 0.2 1.7 HIC
## ECUADOR 0.1948 [0.1944; 0.1951] 0.1 1.7 UMIC
## EGYPT 0.1064 [0.1063; 0.1066] 0.2 1.7 LMIC
## ESTONIA 0.2020 [0.2007; 0.2032] 0.0 1.7 HIC
## FINLAND 4.0004 [3.9976; 4.0032] 0.6 1.7 HIC
## FRANCE 3.3969 [3.3961; 3.3976] 6.2 1.7 HIC
## GERMANY 2.6368 [2.6362; 2.6374] 6.2 1.7 HIC
## GREECE 2.2484 [2.2470; 2.2499] 0.7 1.7 HIC
## HUNGARY 0.7084 [0.7075; 0.7092] 0.2 1.7 HIC
## INDIA 0.1154 [0.1154; 0.1154] 4.0 1.7 LMIC
## IRELAND 2.7226 [2.7200; 2.7251] 0.4 1.7 HIC
## ITALY 1.3687 [1.3682; 1.3692] 2.3 1.7 HIC
## JAPAN 0.0512 [0.0511; 0.0513] 0.2 1.7 HIC
## JORDAN 0.1265 [0.1261; 0.1270] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1175 [0.1168; 0.1182] 0.0 1.7 HIC
## LATVIA 0.4164 [0.4150; 0.4178] 0.0 1.7 HIC
## LEBANON 0.4021 [0.4012; 0.4031] 0.1 1.7 UMIC
## LITHUANIA 0.3440 [0.3430; 0.3451] 0.0 1.7 HIC
## LUXEMBOURG 3.2514 [3.2430; 3.2598] 0.0 1.7 HIC
## MEXICO 0.2788 [0.2786; 0.2789] 0.9 1.7 UMIC
## MOROCCO 0.0236 [0.0235; 0.0237] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.9582 [0.9566; 0.9597] 0.1 1.7 HIC
## NORWAY 3.3147 [3.3120; 3.3174] 0.5 1.7 HIC
## PAKISTAN 0.0973 [0.0972; 0.0974] 0.5 1.7 LMIC
## PERU 0.0497 [0.0495; 0.0498] 0.0 1.7 UMIC
## PHILIPPINES 0.0511 [0.0511; 0.0512] 0.1 1.7 LMIC
## POLAND 0.1501 [0.1499; 0.1503] 0.2 1.7 HIC
## PORTUGAL 3.1225 [3.1207; 3.1243] 1.0 1.7 HIC
## PUERTO RICO 4.7194 [4.7157; 4.7231] 0.5 1.7 HIC
## ROMANIA 0.3023 [0.3019; 0.3026] 0.2 1.7 UMIC
## RUSSIA 0.0259 [0.0258; 0.0259] 0.1 1.7 UMIC
## SAUDI ARABIA 0.3616 [0.3612; 0.3620] 0.3 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.3743 [1.3727; 1.3760] 0.2 1.7 HIC
## SLOVENIA 1.5309 [1.5280; 1.5337] 0.1 1.7 HIC
## SOUTH AFRICA 0.1159 [0.1158; 0.1161] 0.2 1.7 UMIC
## SOUTH KOREA 0.9479 [0.9475; 0.9484] 1.4 1.7 HIC
## SPAIN 3.7663 [3.7654; 3.7673] 5.1 1.7 HIC
## SWEDEN 3.2751 [3.2731; 3.2770] 0.9 1.7 HIC
## SWITZERLAND 1.7979 [1.7963; 1.7995] 0.4 1.7 HIC
## TUNISIA 0.1223 [0.1220; 0.1227] 0.0 1.7 LMIC
## TÜRKIYE 1.3790 [1.3785; 1.3794] 2.8 1.7 UMIC
## UNITED ARAB EMIRATES 0.3101 [0.3094; 0.3108] 0.1 1.7 HIC
## UNITED KINGDOM 2.8783 [2.8776; 2.8790] 5.2 1.7 HIC
## UNITED STATES 5.7693 [5.7689; 5.7698] 51.0 1.7 HIC
## URUGUAY 0.2539 [0.2530; 0.2548] 0.0 1.7 HIC
## VENEZUELA 0.6793 [0.6788; 0.6798] 0.5 1.7 UMIC
##
## Number of studies combined: k = 58
##
## rate 95%-CI z p-value
## Common effect model 3.0300 [3.0298; 3.0301] 39251.99 0
## Random effects model 0.4472 [0.3232; 0.6188] -4.86 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.5924 [1.3227; 4.0846]; tau = 1.2619 [1.1501; 2.0210]
## I^2 = 100.0%; H = 5027.57
##
## Test of heterogeneity:
## Q d.f. p-value
## 1440758045.75 57 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 36 4.0682 [4.0679; 4.0684] 350675645.52 100.0%
## income = UMIC 16 0.5350 [0.5349; 0.5351] 91717462.00 100.0%
## income = LMIC 6 0.1098 [0.1098; 0.1099] 1856400.74 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 996508537.49 2 0
## Within groups 444249508.27 55 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 36 1.0582 [0.8443; 1.3264] 0.4782 0.6915
## income = UMIC 16 0.1258 [0.0651; 0.2433] 1.8113 1.3459
## income = LMIC 6 0.0749 [0.0588; 0.0954] 0.0915 0.3025
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 252.48 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.2753 [0.2750; 0.2756] 0.2 1.7 UMIC
## ARGENTINA 0.2916 [0.2913; 0.2918] 0.3 1.7 UMIC
## AUSTRALIA 0.9923 [0.9916; 0.9930] 0.5 1.7 HIC
## AUSTRIA 2.3836 [2.3819; 2.3853] 0.5 1.7 HIC
## BELARUS 0.0133 [0.0132; 0.0135] 0.0 1.7 UMIC
## BELGIUM 1.5252 [1.5240; 1.5264] 0.4 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0739 [0.0739; 0.0740] 0.4 1.7 UMIC
## BULGARIA 0.2093 [0.2088; 0.2099] 0.0 1.7 UMIC
## CANADA 4.5795 [4.5783; 4.5807] 3.9 1.7 HIC
## CHILE 0.2263 [0.2259; 0.2266] 0.1 1.7 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0840 [0.0839; 0.0841] 0.1 1.7 UMIC
## CROATIA 0.3490 [0.3481; 0.3500] 0.0 1.7 HIC
## CZECH REPUBLIC 1.0415 [1.0405; 1.0425] 0.3 1.7 HIC
## ECUADOR 0.2420 [0.2416; 0.2424] 0.1 1.7 UMIC
## EGYPT 0.1499 [0.1497; 0.1500] 0.3 1.7 LMIC
## ESTONIA 0.2543 [0.2529; 0.2557] 0.0 1.7 HIC
## FINLAND 4.5997 [4.5967; 4.6027] 0.6 1.7 HIC
## FRANCE 3.6916 [3.6908; 3.6924] 5.8 1.7 HIC
## GERMANY 3.0494 [3.0488; 3.0500] 6.2 1.7 HIC
## GREECE 2.6236 [2.6221; 2.6252] 0.7 1.7 HIC
## HUNGARY 0.8272 [0.8262; 0.8281] 0.2 1.7 HIC
## INDIA 0.1569 [0.1569; 0.1569] 4.8 1.7 LMIC
## IRELAND 3.3455 [3.3427; 3.3483] 0.4 1.7 HIC
## ITALY 1.4985 [1.4980; 1.4990] 2.2 1.7 HIC
## JAPAN 0.0746 [0.0745; 0.0747] 0.2 1.7 HIC
## JORDAN 0.1551 [0.1546; 0.1556] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1514 [0.1507; 0.1522] 0.0 1.7 HIC
## LATVIA 0.5005 [0.4989; 0.5021] 0.0 1.7 HIC
## LEBANON 0.5855 [0.5844; 0.5867] 0.1 1.7 UMIC
## LITHUANIA 0.4013 [0.4002; 0.4025] 0.0 1.7 HIC
## LUXEMBOURG 3.3859 [3.3774; 3.3943] 0.0 1.7 HIC
## MEXICO 0.2783 [0.2782; 0.2785] 0.8 1.7 UMIC
## MOROCCO 0.0386 [0.0385; 0.0387] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.1594 [1.1577; 1.1611] 0.1 1.7 HIC
## NORWAY 3.4867 [3.4840; 3.4895] 0.4 1.7 HIC
## PAKISTAN 0.1376 [0.1375; 0.1377] 0.6 1.7 LMIC
## PERU 0.0548 [0.0547; 0.0549] 0.0 1.7 UMIC
## PHILIPPINES 0.0619 [0.0618; 0.0620] 0.1 1.7 LMIC
## POLAND 0.1725 [0.1723; 0.1727] 0.2 1.7 HIC
## PORTUGAL 3.3821 [3.3803; 3.3840] 0.9 1.7 HIC
## PUERTO RICO 5.7197 [5.7156; 5.7238] 0.5 1.7 HIC
## ROMANIA 0.5760 [0.5754; 0.5765] 0.3 1.7 UMIC
## RUSSIA 0.0423 [0.0422; 0.0423] 0.2 1.7 UMIC
## SAUDI ARABIA 0.4092 [0.4088; 0.4096] 0.3 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.8100 [1.8082; 1.8119] 0.2 1.7 HIC
## SLOVENIA 1.7773 [1.7743; 1.7803] 0.1 1.7 HIC
## SOUTH AFRICA 0.1586 [0.1584; 0.1588] 0.2 1.7 UMIC
## SOUTH KOREA 1.1294 [1.1289; 1.1299] 1.4 1.7 HIC
## SPAIN 4.3576 [4.3566; 4.3586] 5.1 1.7 HIC
## SWEDEN 3.8172 [3.8152; 3.8193] 0.9 1.7 HIC
## SWITZERLAND 1.9673 [1.9657; 1.9690] 0.4 1.7 HIC
## TUNISIA 0.2006 [0.2002; 0.2011] 0.1 1.7 LMIC
## TÜRKIYE 1.7266 [1.7261; 1.7271] 3.1 1.7 UMIC
## UNITED ARAB EMIRATES 0.4496 [0.4488; 0.4503] 0.1 1.7 HIC
## UNITED KINGDOM 3.6275 [3.6267; 3.6283] 5.8 1.7 HIC
## UNITED STATES 6.3217 [6.3212; 6.3221] 48.9 1.7 HIC
## URUGUAY 0.4441 [0.4430; 0.4453] 0.0 1.7 HIC
## VENEZUELA 0.7331 [0.7326; 0.7337] 0.5 1.7 UMIC
##
## Number of studies combined: k = 58
##
## rate 95%-CI z p-value
## Common effect model 3.2542 [3.2540; 3.2544] 44884.94 0
## Random effects model 0.5782 [0.4185; 0.7989] -3.32 0.0009
##
## Quantifying heterogeneity:
## tau^2 = 1.5787 [1.2430; 3.7541]; tau = 1.2565 [1.1149; 1.9376]
## I^2 = 100.0%; H = 5452.35
##
## Test of heterogeneity:
## Q d.f. p-value
## 1694503010.98 57 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 36 4.5092 [4.5090; 4.5095] 383747163.51 100.0%
## income = UMIC 16 0.6468 [0.6466; 0.6469] 116591378.93 100.0%
## income = LMIC 6 0.1502 [0.1502; 0.1503] 2739455.11 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1191425013.44 2 0
## Within groups 503077997.54 55 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 36 1.2632 [1.0140; 1.5735] 0.4523 0.6725
## income = UMIC 16 0.1872 [0.0979; 0.3579] 1.7491 1.3225
## income = LMIC 6 0.1076 [0.0840; 0.1377] 0.0952 0.3086
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 219.20 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.3584 [0.3581; 0.3587] 0.3 1.7 UMIC
## ARGENTINA 0.4124 [0.4120; 0.4127] 0.4 1.7 UMIC
## AUSTRALIA 1.1396 [1.1388; 1.1403] 0.5 1.7 HIC
## AUSTRIA 2.8554 [2.8535; 2.8573] 0.5 1.7 HIC
## BELARUS 0.0170 [0.0168; 0.0171] 0.0 1.7 UMIC
## BELGIUM 2.1376 [2.1362; 2.1390] 0.5 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.1212 [0.1211; 0.1213] 0.5 1.7 UMIC
## BULGARIA 0.3133 [0.3127; 0.3140] 0.1 1.7 UMIC
## CANADA 5.1363 [5.1350; 5.1376] 3.8 1.7 HIC
## CHILE 0.2580 [0.2576; 0.2584] 0.1 1.7 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0760 [0.0759; 0.0762] 0.1 1.7 UMIC
## CROATIA 0.4411 [0.4401; 0.4421] 0.0 1.7 HIC
## CZECH REPUBLIC 1.3238 [1.3226; 1.3249] 0.3 1.7 HIC
## ECUADOR 0.2917 [0.2912; 0.2921] 0.1 1.7 UMIC
## EGYPT 0.2397 [0.2396; 0.2399] 0.4 1.7 LMIC
## ESTONIA 0.4373 [0.4355; 0.4392] 0.0 1.7 HIC
## FINLAND 5.0713 [5.0681; 5.0744] 0.6 1.7 HIC
## FRANCE 4.0099 [4.0091; 4.0108] 5.4 1.7 HIC
## GERMANY 3.4155 [3.4149; 3.4162] 6.0 1.7 HIC
## GREECE 2.8252 [2.8236; 2.8269] 0.7 1.7 HIC
## HUNGARY 1.0039 [1.0029; 1.0049] 0.2 1.7 HIC
## INDIA 0.1918 [0.1918; 0.1919] 5.1 1.7 LMIC
## IRELAND 3.7614 [3.7584; 3.7643] 0.4 1.7 HIC
## ITALY 1.6730 [1.6725; 1.6736] 2.1 1.7 HIC
## JAPAN 0.1925 [0.1924; 0.1926] 0.5 1.7 HIC
## JORDAN 0.1833 [0.1828; 0.1838] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1562 [0.1555; 0.1569] 0.0 1.7 HIC
## LATVIA 0.5646 [0.5630; 0.5663] 0.0 1.7 HIC
## LEBANON 0.6460 [0.6448; 0.6471] 0.1 1.7 UMIC
## LITHUANIA 0.5051 [0.5037; 0.5064] 0.0 1.7 HIC
## LUXEMBOURG 3.6404 [3.6317; 3.6491] 0.0 1.7 HIC
## MEXICO 0.2904 [0.2902; 0.2906] 0.7 1.7 UMIC
## MOROCCO 0.0432 [0.0431; 0.0433] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.3959 [1.3940; 1.3977] 0.1 1.7 HIC
## NORWAY 3.8237 [3.8209; 3.8266] 0.4 1.7 HIC
## PAKISTAN 0.1573 [0.1572; 0.1574] 0.6 1.7 LMIC
## PERU 0.0700 [0.0698; 0.0702] 0.0 1.7 UMIC
## PHILIPPINES 0.0664 [0.0663; 0.0665] 0.1 1.7 LMIC
## POLAND 0.1985 [0.1982; 0.1987] 0.2 1.7 HIC
## PORTUGAL 3.7985 [3.7965; 3.8004] 0.9 1.7 HIC
## PUERTO RICO 6.2625 [6.2582; 6.2668] 0.5 1.7 HIC
## ROMANIA 0.5799 [0.5794; 0.5805] 0.3 1.7 UMIC
## RUSSIA 0.0856 [0.0855; 0.0857] 0.3 1.7 UMIC
## SAUDI ARABIA 0.4869 [0.4865; 0.4873] 0.3 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 2.3333 [2.3312; 2.3355] 0.3 1.7 HIC
## SLOVENIA 2.0489 [2.0457; 2.0522] 0.1 1.7 HIC
## SOUTH AFRICA 0.1825 [0.1823; 0.1827] 0.2 1.7 UMIC
## SOUTH KOREA 1.2561 [1.2556; 1.2566] 1.3 1.7 HIC
## SPAIN 4.8059 [4.8049; 4.8069] 4.9 1.7 HIC
## SWEDEN 4.1598 [4.1577; 4.1620] 0.8 1.7 HIC
## SWITZERLAND 2.2679 [2.2661; 2.2696] 0.4 1.7 HIC
## TUNISIA 0.2414 [0.2409; 0.2418] 0.1 1.7 LMIC
## TÜRKIYE 2.1526 [2.1521; 2.1532] 3.4 1.7 UMIC
## UNITED ARAB EMIRATES 0.7453 [0.7443; 0.7462] 0.1 1.7 HIC
## UNITED KINGDOM 4.5325 [4.5317; 4.5334] 6.2 1.7 HIC
## UNITED STATES 7.2737 [7.2732; 7.2742] 48.5 1.7 HIC
## URUGUAY 0.5483 [0.5470; 0.5496] 0.0 1.7 HIC
## VENEZUELA 0.7677 [0.7672; 0.7682] 0.5 1.7 UMIC
##
## Number of studies combined: k = 58
##
## rate 95%-CI z p-value
## Common effect model 3.6422 [3.6420; 3.6424] 53178.65 0
## Random effects model 0.7050 [0.5084; 0.9776] -2.10 0.0361
##
## Quantifying heterogeneity:
## tau^2 = 1.6138 [1.1857; 3.5417]; tau = 1.2703 [1.0889; 1.8820]
## I^2 = 100.0%; H = 5977.24
##
## Test of heterogeneity:
## Q d.f. p-value
## 2036464570.62 57 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 36 5.1182 [5.1179; 5.1184] 477089993.44 100.0%
## income = UMIC 16 0.7636 [0.7635; 0.7638] 150529086.66 100.0%
## income = LMIC 6 0.1859 [0.1859; 0.1859] 4408722.89 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1404436767.63 2 0
## Within groups 632027802.99 55 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 36 1.5265 [1.2156; 1.9168] 0.4860 0.6971
## income = UMIC 16 0.2331 [0.1206; 0.4506] 1.8089 1.3450
## income = LMIC 6 0.1308 [0.0992; 0.1725] 0.1197 0.3460
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 186.97 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5023 [0.5019; 0.5026] 0.4 1.6 UMIC
## ARGENTINA 0.6236 [0.6232; 0.6240] 0.5 1.6 UMIC
## AUSTRALIA 1.2870 [1.2863; 1.2878] 0.6 1.6 HIC
## AUSTRIA 3.2696 [3.2676; 3.2716] 0.5 1.6 HIC
## BELARUS 0.0177 [0.0175; 0.0178] 0.0 1.6 UMIC
## BELGIUM 2.5505 [2.5490; 2.5521] 0.5 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0490 [0.0486; 0.0494] 0.0 1.6 UMIC
## BRAZIL 0.1618 [0.1617; 0.1619] 0.6 1.6 UMIC
## BULGARIA 0.4756 [0.4748; 0.4764] 0.1 1.6 UMIC
## CANADA 5.6068 [5.6055; 5.6081] 3.7 1.6 HIC
## CHILE 0.3141 [0.3137; 0.3146] 0.1 1.6 HIC
## CHINA 0.0002 [0.0002; 0.0002] 0.0 1.6 UMIC
## COLOMBIA 0.0820 [0.0819; 0.0821] 0.1 1.6 UMIC
## CROATIA 0.4605 [0.4594; 0.4616] 0.0 1.6 HIC
## CZECH REPUBLIC 1.7416 [1.7403; 1.7429] 0.4 1.6 HIC
## ECUADOR 0.3322 [0.3317; 0.3327] 0.1 1.6 UMIC
## EGYPT 0.3250 [0.3248; 0.3252] 0.5 1.6 LMIC
## ESTONIA 0.5211 [0.5191; 0.5231] 0.0 1.6 HIC
## FINLAND 5.4366 [5.4334; 5.4399] 0.6 1.6 HIC
## FRANCE 4.1623 [4.1614; 4.1631] 5.1 1.6 HIC
## GERMANY 3.6956 [3.6949; 3.6963] 5.8 1.6 HIC
## GREECE 3.0761 [3.0744; 3.0778] 0.6 1.6 HIC
## HUNGARY 1.1992 [1.1981; 1.2003] 0.2 1.6 HIC
## INDIA 0.2357 [0.2356; 0.2357] 5.7 1.6 LMIC
## IRELAND 4.4967 [4.4935; 4.4999] 0.4 1.6 HIC
## ITALY 1.8488 [1.8482; 1.8494] 2.1 1.6 HIC
## JAPAN 0.7095 [0.7093; 0.7097] 1.8 1.6 HIC
## JORDAN 0.2052 [0.2047; 0.2057] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0285 [0.0283; 0.0286] 0.0 1.6 UMIC
## KUWAIT 0.2263 [0.2254; 0.2271] 0.0 1.6 HIC
## LATVIA 0.6788 [0.6770; 0.6807] 0.0 1.6 HIC
## LEBANON 0.7612 [0.7600; 0.7625] 0.1 1.6 UMIC
## LITHUANIA 0.7170 [0.7154; 0.7186] 0.0 1.6 HIC
## LUXEMBOURG 3.7951 [3.7863; 3.8039] 0.0 1.6 HIC
## MEXICO 0.2666 [0.2664; 0.2668] 0.6 1.6 UMIC
## MOROCCO 0.0533 [0.0531; 0.0534] 0.0 1.6 LMIC
## NETHERLANDS 2.3112 [2.3100; 2.3124] 0.7 1.6 HIC
## NEW ZEALAND 1.6199 [1.6180; 1.6219] 0.1 1.6 HIC
## NORWAY 4.3460 [4.3430; 4.3490] 0.4 1.6 HIC
## PAKISTAN 0.1948 [0.1947; 0.1949] 0.7 1.6 LMIC
## PERU 0.0813 [0.0811; 0.0815] 0.0 1.6 UMIC
## PHILIPPINES 0.0713 [0.0712; 0.0714] 0.1 1.6 LMIC
## POLAND 0.2352 [0.2350; 0.2355] 0.2 1.6 HIC
## PORTUGAL 3.8604 [3.8585; 3.8624] 0.8 1.6 HIC
## PUERTO RICO 8.0259 [8.0210; 8.0308] 0.6 1.6 HIC
## ROMANIA 0.7046 [0.7040; 0.7052] 0.3 1.6 UMIC
## RUSSIA 0.1567 [0.1566; 0.1568] 0.4 1.6 UMIC
## SAUDI ARABIA 0.5929 [0.5924; 0.5933] 0.3 1.6 HIC
## SERBIA 0.0947 [0.0944; 0.0950] 0.0 1.6 UMIC
## SLOVAKIA 2.9189 [2.9166; 2.9213] 0.3 1.6 HIC
## SLOVENIA 2.2320 [2.2286; 2.2354] 0.1 1.6 HIC
## SOUTH AFRICA 0.2093 [0.2091; 0.2095] 0.2 1.6 UMIC
## SOUTH KOREA 1.3683 [1.3677; 1.3688] 1.3 1.6 HIC
## SPAIN 5.3064 [5.3053; 5.3075] 4.8 1.6 HIC
## SWEDEN 4.4881 [4.4859; 4.4903] 0.8 1.6 HIC
## SWITZERLAND 2.5087 [2.5069; 2.5106] 0.4 1.6 HIC
## TUNISIA 0.2704 [0.2699; 0.2709] 0.1 1.6 LMIC
## TÜRKIYE 2.7104 [2.7098; 2.7111] 3.8 1.6 UMIC
## UNITED ARAB EMIRATES 0.8151 [0.8141; 0.8160] 0.1 1.6 HIC
## UNITED KINGDOM 5.4146 [5.4136; 5.4155] 6.7 1.6 HIC
## UNITED STATES 7.4197 [7.4192; 7.4202] 44.7 1.6 HIC
## URUGUAY 0.6987 [0.6973; 0.7002] 0.0 1.6 HIC
## VENEZUELA 0.8805 [0.8799; 0.8810] 0.5 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 3.6346 [3.6345; 3.6348] 56087.65 0
## Random effects model 0.6600 [0.4870; 0.8943] -2.68 0.0073
##
## Quantifying heterogeneity:
## tau^2 = 1.5140 [1.1831; 3.3788]; tau = 1.2305 [1.0877; 1.8382]
## I^2 = 100.0%; H = 5996.78
##
## Test of heterogeneity:
## Q d.f. p-value
## 2229604364.46 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 5.1847 [5.1844; 5.1849] 519101290.50 100.0%
## income = UMIC 20 0.9359 [0.9358; 0.9361] 205118593.15 100.0%
## income = LMIC 6 0.2304 [0.2304; 0.2305] 6429926.08 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1498954554.73 2 0
## Within groups 730649809.72 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.8301 [1.4710; 2.2769] 0.4597 0.6780
## income = UMIC 20 0.1537 [0.0836; 0.2824] 1.9275 1.3884
## income = LMIC 6 0.1576 [0.1172; 0.2119] 0.1370 0.3701
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 195.50 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5436 [0.5432; 0.5440] 0.4 1.6 UMIC
## ARGENTINA 0.8075 [0.8071; 0.8080] 0.6 1.6 UMIC
## AUSTRALIA 1.4720 [1.4712; 1.4729] 0.6 1.6 HIC
## AUSTRIA 3.6998 [3.6977; 3.7020] 0.6 1.6 HIC
## BELARUS 0.0198 [0.0196; 0.0199] 0.0 1.6 UMIC
## BELGIUM 2.7344 [2.7328; 2.7360] 0.5 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.1042 [0.1036; 0.1047] 0.0 1.6 UMIC
## BRAZIL 0.2003 [0.2002; 0.2004] 0.7 1.6 UMIC
## BULGARIA 0.3382 [0.3376; 0.3389] 0.0 1.6 UMIC
## CANADA 6.1915 [6.1901; 6.1929] 3.9 1.6 HIC
## CHILE 0.4029 [0.4024; 0.4033] 0.1 1.6 HIC
## CHINA 0.0011 [0.0011; 0.0011] 0.0 1.6 UMIC
## COLOMBIA 0.0904 [0.0902; 0.0905] 0.1 1.6 UMIC
## CROATIA 0.5082 [0.5071; 0.5094] 0.0 1.6 HIC
## CZECH REPUBLIC 1.8445 [1.8431; 1.8458] 0.4 1.6 HIC
## ECUADOR 0.3715 [0.3710; 0.3720] 0.1 1.6 UMIC
## EGYPT 0.4519 [0.4517; 0.4522] 0.7 1.6 LMIC
## ESTONIA 0.6499 [0.6477; 0.6522] 0.0 1.6 HIC
## FINLAND 5.8920 [5.8886; 5.8954] 0.6 1.6 HIC
## FRANCE 4.4897 [4.4888; 4.4906] 5.1 1.6 HIC
## GERMANY 3.9894 [3.9886; 3.9901] 5.8 1.6 HIC
## GREECE 2.9461 [2.9444; 2.9478] 0.6 1.6 HIC
## HUNGARY 1.3768 [1.3756; 1.3780] 0.2 1.6 HIC
## INDIA 0.2661 [0.2661; 0.2662] 6.1 1.6 LMIC
## IRELAND 5.5402 [5.5366; 5.5437] 0.5 1.6 HIC
## ITALY 1.9265 [1.9260; 1.9271] 2.1 1.6 HIC
## JAPAN 1.1690 [1.1687; 1.1694] 2.7 1.6 HIC
## JORDAN 0.2711 [0.2705; 0.2717] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0437 [0.0436; 0.0439] 0.0 1.6 UMIC
## KUWAIT 0.3421 [0.3411; 0.3431] 0.0 1.6 HIC
## LATVIA 0.8875 [0.8853; 0.8896] 0.0 1.6 HIC
## LEBANON 0.7933 [0.7921; 0.7945] 0.1 1.6 UMIC
## LITHUANIA 0.8401 [0.8384; 0.8418] 0.0 1.6 HIC
## LUXEMBOURG 4.0584 [4.0494; 4.0674] 0.0 1.6 HIC
## MEXICO 0.2671 [0.2669; 0.2672] 0.6 1.6 UMIC
## MOROCCO 0.0653 [0.0651; 0.0654] 0.0 1.6 LMIC
## NETHERLANDS 2.5953 [2.5940; 2.5966] 0.8 1.6 HIC
## NEW ZEALAND 1.8964 [1.8943; 1.8985] 0.2 1.6 HIC
## NORWAY 4.6307 [4.6276; 4.6338] 0.4 1.6 HIC
## PAKISTAN 0.2124 [0.2123; 0.2125] 0.7 1.6 LMIC
## PERU 0.0954 [0.0952; 0.0955] 0.1 1.6 UMIC
## PHILIPPINES 0.0757 [0.0756; 0.0758] 0.1 1.6 LMIC
## POLAND 0.2148 [0.2146; 0.2151] 0.1 1.6 HIC
## PORTUGAL 4.3366 [4.3345; 4.3387] 0.8 1.6 HIC
## PUERTO RICO 9.1711 [9.1659; 9.1763] 0.6 1.6 HIC
## ROMANIA 0.7315 [0.7309; 0.7321] 0.3 1.6 UMIC
## RUSSIA 0.3161 [0.3160; 0.3163] 0.8 1.6 UMIC
## SAUDI ARABIA 0.7378 [0.7373; 0.7383] 0.4 1.6 HIC
## SERBIA 0.1341 [0.1337; 0.1345] 0.0 1.6 UMIC
## SLOVAKIA 3.1828 [3.1803; 3.1852] 0.3 1.6 HIC
## SLOVENIA 2.4452 [2.4416; 2.4487] 0.1 1.6 HIC
## SOUTH AFRICA 0.2379 [0.2377; 0.2381] 0.2 1.6 UMIC
## SOUTH KOREA 1.7305 [1.7299; 1.7311] 1.6 1.6 HIC
## SPAIN 5.6183 [5.6172; 5.6194] 4.7 1.6 HIC
## SWEDEN 4.5816 [4.5793; 4.5838] 0.8 1.6 HIC
## SWITZERLAND 2.7129 [2.7110; 2.7148] 0.4 1.6 HIC
## TUNISIA 0.3903 [0.3897; 0.3909] 0.1 1.6 LMIC
## TÜRKIYE 3.1355 [3.1348; 3.1361] 4.2 1.6 UMIC
## UNITED ARAB EMIRATES 0.3576 [0.3569; 0.3582] 0.1 1.6 HIC
## UNITED KINGDOM 6.4225 [6.4215; 6.4235] 7.4 1.6 HIC
## UNITED STATES 7.2591 [7.2587; 7.2596] 41.0 1.6 HIC
## URUGUAY 1.0938 [1.0920; 1.0957] 0.1 1.6 HIC
## VENEZUELA 1.0497 [1.0491; 1.0503] 0.6 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 3.6179 [3.6177; 3.6181] 57983.67 0
## Random effects model 0.7830 [0.5838; 1.0502] -1.63 0.1024
##
## Quantifying heterogeneity:
## tau^2 = 1.4133 [1.1589; 3.2512]; tau = 1.1888 [1.0765; 1.8031]
## I^2 = 100.0%; H = 6124.61
##
## Test of heterogeneity:
## Q d.f. p-value
## 2325672672.82 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 5.2437 [5.2435; 5.2440] 485227562.02 100.0%
## income = UMIC 20 1.0484 [1.0482; 1.0485] 255538863.39 100.0%
## income = LMIC 6 0.2668 [0.2667; 0.2668] 10778847.67 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1574127399.74 2 0
## Within groups 751545273.08 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 2.0442 [1.6720; 2.4991] 0.3889 0.6236
## income = UMIC 20 0.2024 [0.1100; 0.3726] 1.9368 1.3917
## income = LMIC 6 0.1914 [0.1355; 0.2706] 0.1870 0.4324
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 162.65 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.6123 [0.6119; 0.6128] 0.4 1.6 UMIC
## ARGENTINA 0.9655 [0.9650; 0.9660] 0.6 1.6 UMIC
## AUSTRALIA 3.4622 [3.4610; 3.4635] 1.2 1.6 HIC
## AUSTRIA 4.0037 [4.0015; 4.0059] 0.5 1.6 HIC
## BELARUS 0.0324 [0.0322; 0.0326] 0.0 1.6 UMIC
## BELGIUM 2.7919 [2.7903; 2.7936] 0.5 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.1485 [0.1479; 0.1492] 0.0 1.6 UMIC
## BRAZIL 0.2464 [0.2463; 0.2465] 0.8 1.6 UMIC
## BULGARIA 0.4681 [0.4673; 0.4690] 0.1 1.6 UMIC
## CANADA 7.1289 [7.1274; 7.1304] 3.9 1.6 HIC
## CHILE 0.4888 [0.4882; 0.4893] 0.1 1.6 HIC
## CHINA 0.0017 [0.0017; 0.0017] 0.0 1.6 UMIC
## COLOMBIA 0.1060 [0.1058; 0.1062] 0.1 1.6 UMIC
## CROATIA 0.5424 [0.5413; 0.5436] 0.0 1.6 HIC
## CZECH REPUBLIC 2.1942 [2.1927; 2.1956] 0.4 1.6 HIC
## ECUADOR 0.3806 [0.3801; 0.3811] 0.1 1.6 UMIC
## EGYPT 0.5793 [0.5791; 0.5796] 0.8 1.6 LMIC
## ESTONIA 0.8479 [0.8453; 0.8505] 0.0 1.6 HIC
## FINLAND 6.4343 [6.4307; 6.4378] 0.5 1.6 HIC
## FRANCE 4.7527 [4.7518; 4.7536] 4.7 1.6 HIC
## GERMANY 4.2557 [4.2550; 4.2565] 5.4 1.6 HIC
## GREECE 3.2543 [3.2525; 3.2561] 0.5 1.6 HIC
## HUNGARY 1.5987 [1.5974; 1.6000] 0.2 1.6 HIC
## INDIA 0.2922 [0.2921; 0.2922] 5.8 1.6 LMIC
## IRELAND 6.7554 [6.7515; 6.7593] 0.5 1.6 HIC
## ITALY 2.0471 [2.0465; 2.0477] 1.9 1.6 HIC
## JAPAN 1.5659 [1.5655; 1.5663] 3.1 1.6 HIC
## JORDAN 0.4185 [0.4177; 0.4192] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0482 [0.0480; 0.0484] 0.0 1.6 UMIC
## KUWAIT 0.7306 [0.7291; 0.7321] 0.0 1.6 HIC
## LATVIA 1.1133 [1.1109; 1.1157] 0.0 1.6 HIC
## LEBANON 0.8309 [0.8297; 0.8321] 0.1 1.6 UMIC
## LITHUANIA 1.0096 [1.0078; 1.0115] 0.0 1.6 HIC
## LUXEMBOURG 4.0818 [4.0729; 4.0907] 0.0 1.6 HIC
## MEXICO 0.2726 [0.2724; 0.2727] 0.5 1.6 UMIC
## MOROCCO 0.0781 [0.0780; 0.0783] 0.0 1.6 LMIC
## NETHERLANDS 2.8055 [2.8042; 2.8068] 0.7 1.6 HIC
## NEW ZEALAND 2.1611 [2.1589; 2.1634] 0.2 1.6 HIC
## NORWAY 4.7693 [4.7661; 4.7724] 0.4 1.6 HIC
## PAKISTAN 0.2292 [0.2291; 0.2293] 0.7 1.6 LMIC
## PERU 0.1101 [0.1099; 0.1103] 0.1 1.6 UMIC
## PHILIPPINES 0.0888 [0.0887; 0.0889] 0.1 1.6 LMIC
## POLAND 0.2773 [0.2770; 0.2776] 0.2 1.6 HIC
## PORTUGAL 4.3293 [4.3272; 4.3313] 0.7 1.6 HIC
## PUERTO RICO 9.3277 [9.3224; 9.3330] 0.5 1.6 HIC
## ROMANIA 0.8751 [0.8744; 0.8758] 0.3 1.6 UMIC
## RUSSIA 0.7221 [0.7219; 0.7223] 1.6 1.6 UMIC
## SAUDI ARABIA 1.0581 [1.0575; 1.0587] 0.5 1.6 HIC
## SERBIA 0.2477 [0.2471; 0.2482] 0.0 1.6 UMIC
## SLOVAKIA 3.3849 [3.3824; 3.3875] 0.3 1.6 HIC
## SLOVENIA 2.6081 [2.6045; 2.6118] 0.1 1.6 HIC
## SOUTH AFRICA 0.2529 [0.2527; 0.2532] 0.2 1.6 UMIC
## SOUTH KOREA 1.8725 [1.8719; 1.8731] 1.5 1.6 HIC
## SPAIN 5.9619 [5.9607; 5.9631] 4.3 1.6 HIC
## SWEDEN 5.0218 [5.0194; 5.0241] 0.7 1.6 HIC
## SWITZERLAND 2.9200 [2.9181; 2.9220] 0.4 1.6 HIC
## TUNISIA 0.4522 [0.4516; 0.4529] 0.1 1.6 LMIC
## TÜRKIYE 3.5587 [3.5580; 3.5594] 4.2 1.6 UMIC
## UNITED ARAB EMIRATES 0.5663 [0.5655; 0.5671] 0.1 1.6 HIC
## UNITED KINGDOM 7.7446 [7.7435; 7.7458] 7.8 1.6 HIC
## UNITED STATES 8.2747 [8.2741; 8.2752] 40.6 1.6 HIC
## URUGUAY 1.1789 [1.1770; 1.1809] 0.1 1.6 HIC
## VENEZUELA 1.2948 [1.2941; 1.2954] 0.6 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 4.0547 [4.0545; 4.0548] 67909.15 0
## Random effects model 0.9482 [0.7084; 1.2692] -0.36 0.7208
##
## Quantifying heterogeneity:
## tau^2 = 1.3944 [1.1092; 3.0849]; tau = 1.1809 [1.0532; 1.7564]
## I^2 = 100.0%; H = 6557.61
##
## Test of heterogeneity:
## Q d.f. p-value
## 2666141965.44 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 5.9161 [5.9159; 5.9164] 555584339.04 100.0%
## income = UMIC 20 1.2135 [1.2133; 1.2136] 282363051.14 100.0%
## income = LMIC 6 0.2999 [0.2998; 0.2999] 16157481.84 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1812037093.42 2 0
## Within groups 854104872.02 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 2.4125 [1.9738; 2.9488] 0.3880 0.6229
## income = UMIC 20 0.2603 [0.1488; 0.4552] 1.6270 1.2755
## income = LMIC 6 0.2226 [0.1509; 0.3283] 0.2357 0.4855
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 147.28 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.8777 [ 0.8772; 0.8782] 0.5 1.6 UMIC
## ARGENTINA 1.0803 [ 1.0798; 1.0808] 0.6 1.6 UMIC
## AUSTRALIA 5.7009 [ 5.6993; 5.7025] 1.9 1.6 HIC
## AUSTRIA 4.2938 [ 4.2915; 4.2961] 0.5 1.6 HIC
## BELARUS 0.0433 [ 0.0431; 0.0436] 0.0 1.6 UMIC
## BELGIUM 3.0329 [ 3.0312; 3.0346] 0.5 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.1600 [ 0.1593; 0.1607] 0.0 1.6 UMIC
## BRAZIL 0.2972 [ 0.2971; 0.2973] 0.8 1.6 UMIC
## BULGARIA 0.5793 [ 0.5784; 0.5802] 0.1 1.6 UMIC
## CANADA 7.8131 [ 7.8116; 7.8146] 3.8 1.6 HIC
## CHILE 0.5859 [ 0.5853; 0.5865] 0.1 1.6 HIC
## CHINA 0.0023 [ 0.0023; 0.0023] 0.0 1.6 UMIC
## COLOMBIA 0.1232 [ 0.1230; 0.1233] 0.1 1.6 UMIC
## CROATIA 0.6006 [ 0.5994; 0.6018] 0.0 1.6 HIC
## CZECH REPUBLIC 2.4782 [ 2.4766; 2.4797] 0.4 1.6 HIC
## ECUADOR 0.4213 [ 0.4207; 0.4218] 0.1 1.6 UMIC
## EGYPT 0.7263 [ 0.7261; 0.7266] 0.9 1.6 LMIC
## ESTONIA 1.0802 [ 1.0772; 1.0831] 0.0 1.6 HIC
## FINLAND 6.1984 [ 6.1950; 6.2019] 0.5 1.6 HIC
## FRANCE 5.1064 [ 5.1055; 5.1074] 4.5 1.6 HIC
## GERMANY 4.4998 [ 4.4991; 4.5006] 5.0 1.6 HIC
## GREECE 3.2833 [ 3.2815; 3.2851] 0.5 1.6 HIC
## HUNGARY 1.7801 [ 1.7787; 1.7815] 0.2 1.6 HIC
## INDIA 0.3310 [ 0.3309; 0.3310] 5.9 1.6 LMIC
## IRELAND 7.3382 [ 7.3342; 7.3423] 0.5 1.6 HIC
## ITALY 2.1941 [ 2.1935; 2.1947] 1.8 1.6 HIC
## JAPAN 2.0472 [ 2.0468; 2.0476] 3.6 1.6 HIC
## JORDAN 0.5784 [ 0.5776; 0.5793] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0660 [ 0.0658; 0.0662] 0.0 1.6 UMIC
## KUWAIT 0.6781 [ 0.6767; 0.6794] 0.0 1.6 HIC
## LATVIA 1.3425 [ 1.3398; 1.3451] 0.0 1.6 HIC
## LEBANON 0.9179 [ 0.9167; 0.9192] 0.1 1.6 UMIC
## LITHUANIA 1.1894 [ 1.1874; 1.1915] 0.0 1.6 HIC
## LUXEMBOURG 4.2040 [ 4.1951; 4.2129] 0.0 1.6 HIC
## MEXICO 0.2951 [ 0.2949; 0.2953] 0.5 1.6 UMIC
## MOROCCO 0.0859 [ 0.0858; 0.0861] 0.0 1.6 LMIC
## NETHERLANDS 3.1296 [ 3.1282; 3.1310] 0.7 1.6 HIC
## NEW ZEALAND 2.4664 [ 2.4640; 2.4688] 0.2 1.6 HIC
## NORWAY 4.9606 [ 4.9574; 4.9638] 0.4 1.6 HIC
## PAKISTAN 0.2485 [ 0.2483; 0.2486] 0.7 1.6 LMIC
## PERU 0.1205 [ 0.1203; 0.1207] 0.0 1.6 UMIC
## PHILIPPINES 0.0921 [ 0.0920; 0.0922] 0.1 1.6 LMIC
## POLAND 0.3655 [ 0.3652; 0.3659] 0.2 1.6 HIC
## PORTUGAL 4.8523 [ 4.8501; 4.8545] 0.7 1.6 HIC
## PUERTO RICO 11.1148 [11.1090; 11.1207] 0.5 1.6 HIC
## ROMANIA 0.9854 [ 0.9847; 0.9861] 0.3 1.6 UMIC
## RUSSIA 0.9267 [ 0.9264; 0.9270] 1.8 1.6 UMIC
## SAUDI ARABIA 1.3930 [ 1.3923; 1.3936] 0.6 1.6 HIC
## SERBIA 0.3969 [ 0.3962; 0.3976] 0.0 1.6 UMIC
## SLOVAKIA 3.6777 [ 3.6750; 3.6803] 0.3 1.6 HIC
## SLOVENIA 2.9582 [ 2.9543; 2.9621] 0.1 1.6 HIC
## SOUTH AFRICA 0.2727 [ 0.2724; 0.2729] 0.2 1.6 UMIC
## SOUTH KOREA 2.0337 [ 2.0331; 2.0344] 1.4 1.6 HIC
## SPAIN 6.2468 [ 6.2456; 6.2480] 4.0 1.6 HIC
## SWEDEN 5.4543 [ 5.4518; 5.4567] 0.7 1.6 HIC
## SWITZERLAND 3.1593 [ 3.1573; 3.1613] 0.4 1.6 HIC
## TUNISIA 0.5052 [ 0.5045; 0.5059] 0.1 1.6 LMIC
## TÜRKIYE 4.0545 [ 4.0538; 4.0553] 4.3 1.6 UMIC
## UNITED ARAB EMIRATES 0.8103 [ 0.8094; 0.8113] 0.1 1.6 HIC
## UNITED KINGDOM 9.2028 [ 9.2016; 9.2040] 8.3 1.6 HIC
## UNITED STATES 9.0002 [ 8.9996; 9.0007] 39.5 1.6 HIC
## URUGUAY 1.3733 [ 1.3712; 1.3753] 0.1 1.6 HIC
## VENEZUELA 1.5927 [ 1.5919; 1.5934] 0.7 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 4.4292 [4.4290; 4.4294] 76688.35 0
## Random effects model 1.1000 [0.8248; 1.4670] 0.65 0.5164
##
## Quantifying heterogeneity:
## tau^2 = 1.3593 [1.0822; 2.9896]; tau = 1.1659 [1.0403; 1.7290]
## I^2 = 100.0%; H = 6914.66
##
## Test of heterogeneity:
## Q d.f. p-value
## 2964377980.70 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 6.4988 [6.4985; 6.4990] 603340688.47 100.0%
## income = UMIC 20 1.4057 [1.4055; 1.4058] 325689435.53 100.0%
## income = LMIC 6 0.3456 [0.3456; 0.3457] 23740935.32 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2011606921.38 2 0
## Within groups 952771059.32 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 2.7401 [2.2508; 3.3357] 0.3727 0.6105
## income = UMIC 20 0.3174 [0.1839; 0.5478] 1.5506 1.2452
## income = LMIC 6 0.2491 [0.1614; 0.3845] 0.2944 0.5426
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 132.78 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.0435 [ 1.0430; 1.0440] 0.5 1.6 UMIC
## ARGENTINA 1.2359 [ 1.2353; 1.2364] 0.7 1.6 UMIC
## AUSTRALIA 7.0913 [ 7.0895; 7.0931] 2.1 1.6 HIC
## AUSTRIA 4.4185 [ 4.4162; 4.4209] 0.5 1.6 HIC
## BELARUS 0.0582 [ 0.0579; 0.0585] 0.0 1.6 UMIC
## BELGIUM 3.3759 [ 3.3741; 3.3777] 0.5 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.2186 [ 0.2178; 0.2194] 0.0 1.6 UMIC
## BRAZIL 0.3671 [ 0.3669; 0.3672] 0.9 1.6 UMIC
## BULGARIA 0.6741 [ 0.6731; 0.6751] 0.1 1.6 UMIC
## CANADA 8.6784 [ 8.6768; 8.6800] 3.8 1.6 HIC
## CHILE 0.6971 [ 0.6964; 0.6977] 0.2 1.6 HIC
## CHINA 0.0033 [ 0.0033; 0.0033] 0.1 1.6 UMIC
## COLOMBIA 0.1650 [ 0.1648; 0.1652] 0.1 1.6 UMIC
## CROATIA 0.6814 [ 0.6801; 0.6827] 0.0 1.6 HIC
## CZECH REPUBLIC 2.9326 [ 2.9309; 2.9343] 0.4 1.6 HIC
## ECUADOR 0.4820 [ 0.4815; 0.4826] 0.1 1.6 UMIC
## EGYPT 0.9495 [ 0.9492; 0.9498] 1.1 1.6 LMIC
## ESTONIA 1.3609 [ 1.3576; 1.3642] 0.0 1.6 HIC
## FINLAND 6.3539 [ 6.3504; 6.3574] 0.4 1.6 HIC
## FRANCE 5.3377 [ 5.3367; 5.3386] 4.2 1.6 HIC
## GERMANY 4.7284 [ 4.7276; 4.7292] 4.7 1.6 HIC
## GREECE 3.3903 [ 3.3884; 3.3921] 0.4 1.6 HIC
## HUNGARY 1.7637 [ 1.7624; 1.7651] 0.2 1.6 HIC
## INDIA 0.3751 [ 0.3750; 0.3751] 6.0 1.6 LMIC
## IRELAND 9.0513 [ 9.0468; 9.0559] 0.5 1.6 HIC
## ITALY 2.2942 [ 2.2936; 2.2949] 1.7 1.6 HIC
## JAPAN 2.1919 [ 2.1914; 2.1923] 3.4 1.6 HIC
## JORDAN 0.5796 [ 0.5788; 0.5804] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0875 [ 0.0873; 0.0877] 0.0 1.6 UMIC
## KUWAIT 0.7833 [ 0.7818; 0.7847] 0.0 1.6 HIC
## LATVIA 1.6132 [ 1.6103; 1.6162] 0.0 1.6 HIC
## LEBANON 1.0485 [ 1.0472; 1.0498] 0.1 1.6 UMIC
## LITHUANIA 1.2640 [ 1.2619; 1.2661] 0.0 1.6 HIC
## LUXEMBOURG 4.1852 [ 4.1764; 4.1940] 0.0 1.6 HIC
## MEXICO 0.3186 [ 0.3184; 0.3187] 0.5 1.6 UMIC
## MOROCCO 0.1033 [ 0.1032; 0.1035] 0.0 1.6 LMIC
## NETHERLANDS 3.3640 [ 3.3626; 3.3655] 0.7 1.6 HIC
## NEW ZEALAND 2.8854 [ 2.8828; 2.8880] 0.2 1.6 HIC
## NORWAY 5.5764 [ 5.5731; 5.5798] 0.4 1.6 HIC
## PAKISTAN 0.2817 [ 0.2816; 0.2818] 0.7 1.6 LMIC
## PERU 0.1993 [ 0.1991; 0.1996] 0.1 1.6 UMIC
## PHILIPPINES 0.1023 [ 0.1022; 0.1024] 0.1 1.6 LMIC
## POLAND 0.4600 [ 0.4596; 0.4603] 0.2 1.6 HIC
## PORTUGAL 5.0420 [ 5.0397; 5.0443] 0.6 1.6 HIC
## PUERTO RICO 12.5562 [12.5499; 12.5624] 0.5 1.6 HIC
## ROMANIA 1.1013 [ 1.1005; 1.1020] 0.3 1.6 UMIC
## RUSSIA 0.8314 [ 0.8312; 0.8316] 1.5 1.6 UMIC
## SAUDI ARABIA 1.2320 [ 1.2313; 1.2326] 0.5 1.6 HIC
## SERBIA 0.7818 [ 0.7808; 0.7827] 0.1 1.6 UMIC
## SLOVAKIA 4.0659 [ 4.0631; 4.0688] 0.3 1.6 HIC
## SLOVENIA 3.2621 [ 3.2581; 3.2662] 0.1 1.6 HIC
## SOUTH AFRICA 0.2909 [ 0.2907; 0.2912] 0.2 1.6 UMIC
## SOUTH KOREA 2.1810 [ 2.1803; 2.1817] 1.4 1.6 HIC
## SPAIN 6.3410 [ 6.3398; 6.3422] 3.6 1.6 HIC
## SWEDEN 5.8676 [ 5.8651; 5.8701] 0.7 1.6 HIC
## SWITZERLAND 3.4499 [ 3.4478; 3.4520] 0.4 1.6 HIC
## TUNISIA 0.5492 [ 0.5485; 0.5500] 0.1 1.6 LMIC
## TÜRKIYE 4.6726 [ 4.6718; 4.6734] 4.5 1.6 UMIC
## UNITED ARAB EMIRATES 1.0947 [ 1.0936; 1.0958] 0.1 1.6 HIC
## UNITED KINGDOM 10.6487 [10.6474; 10.6500] 8.6 1.6 HIC
## UNITED STATES 10.2815 [10.2810; 10.2821] 40.4 1.6 HIC
## URUGUAY 1.4897 [ 1.4876; 1.4919] 0.1 1.6 HIC
## VENEZUELA 1.0882 [ 1.0875; 1.0888] 0.4 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 4.9804 [4.9803; 4.9806] 87669.28 0
## Random effects model 1.2527 [0.9326; 1.6827] 1.50 0.1345
##
## Quantifying heterogeneity:
## tau^2 = 1.4279 [1.0923; 3.0182]; tau = 1.1950 [1.0451; 1.7373]
## I^2 = 100.0%; H = 7475.74
##
## Test of heterogeneity:
## Q d.f. p-value
## 3464977084.65 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 7.3574 [7.3571; 7.3577] 733861117.91 100.0%
## income = UMIC 20 1.5377 [1.5375; 1.5379] 399553337.63 100.0%
## income = LMIC 6 0.4046 [0.4045; 0.4046] 36909973.46 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2294652655.66 2 0
## Within groups 1170324428.99 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 3.0304 [2.4651; 3.7252] 0.4105 0.6407
## income = UMIC 20 0.3795 [0.2122; 0.6788] 1.7606 1.3269
## income = LMIC 6 0.2890 [0.1772; 0.4714] 0.3738 0.6114
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 105.69 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.4666 [ 1.4659; 1.4672] 0.7 1.6 UMIC
## ARGENTINA 1.2701 [ 1.2696; 1.2707] 0.6 1.6 UMIC
## AUSTRALIA 8.2225 [ 8.2206; 8.2244] 2.2 1.6 HIC
## AUSTRIA 5.3320 [ 5.3295; 5.3345] 0.5 1.6 HIC
## BELARUS 0.0716 [ 0.0713; 0.0719] 0.0 1.6 UMIC
## BELGIUM 4.5408 [ 4.5387; 4.5428] 0.6 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.3015 [ 0.3006; 0.3025] 0.0 1.6 UMIC
## BRAZIL 0.4401 [ 0.4399; 0.4402] 1.0 1.6 UMIC
## BULGARIA 0.9641 [ 0.9629; 0.9653] 0.1 1.6 UMIC
## CANADA 9.6974 [ 9.6957; 9.6990] 3.9 1.6 HIC
## CHILE 0.7907 [ 0.7900; 0.7914] 0.2 1.6 HIC
## CHINA 0.0040 [ 0.0040; 0.0040] 0.1 1.6 UMIC
## COLOMBIA 0.1667 [ 0.1665; 0.1669] 0.1 1.6 UMIC
## CROATIA 1.0166 [ 1.0150; 1.0182] 0.0 1.6 HIC
## CZECH REPUBLIC 3.7812 [ 3.7793; 3.7832] 0.4 1.6 HIC
## ECUADOR 0.4980 [ 0.4975; 0.4986] 0.1 1.6 UMIC
## EGYPT 1.3411 [ 1.3407; 1.3414] 1.4 1.6 LMIC
## ESTONIA 1.7473 [ 1.7435; 1.7510] 0.0 1.6 HIC
## FINLAND 6.8436 [ 6.8400; 6.8473] 0.4 1.6 HIC
## FRANCE 5.4613 [ 5.4604; 5.4623] 3.9 1.6 HIC
## GERMANY 5.0195 [ 5.0187; 5.0203] 4.6 1.6 HIC
## GREECE 3.9338 [ 3.9318; 3.9358] 0.5 1.6 HIC
## HUNGARY 1.8921 [ 1.8906; 1.8935] 0.2 1.6 HIC
## INDIA 0.4165 [ 0.4165; 0.4166] 6.1 1.6 LMIC
## IRELAND 9.7384 [ 9.7338; 9.7431] 0.5 1.6 HIC
## ITALY 2.3378 [ 2.3372; 2.3384] 1.6 1.6 HIC
## JAPAN 2.3809 [ 2.3804; 2.3813] 3.4 1.6 HIC
## JORDAN 0.8999 [ 0.8989; 0.9009] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0714 [ 0.0712; 0.0716] 0.0 1.6 UMIC
## KUWAIT 1.3133 [ 1.3114; 1.3152] 0.1 1.6 HIC
## LATVIA 1.7704 [ 1.7673; 1.7735] 0.0 1.6 HIC
## LEBANON 1.3224 [ 1.3210; 1.3239] 0.1 1.6 UMIC
## LITHUANIA 1.6307 [ 1.6283; 1.6331] 0.1 1.6 HIC
## LUXEMBOURG 4.3268 [ 4.3179; 4.3357] 0.0 1.6 HIC
## MEXICO 0.3396 [ 0.3394; 0.3397] 0.5 1.6 UMIC
## MOROCCO 0.1130 [ 0.1129; 0.1132] 0.0 1.6 LMIC
## NETHERLANDS 3.5819 [ 3.5805; 3.5834] 0.7 1.6 HIC
## NEW ZEALAND 3.3928 [ 3.3901; 3.3956] 0.2 1.6 HIC
## NORWAY 5.7756 [ 5.7722; 5.7790] 0.3 1.6 HIC
## PAKISTAN 0.3136 [ 0.3135; 0.3137] 0.7 1.6 LMIC
## PERU 0.2131 [ 0.2128; 0.2134] 0.1 1.6 UMIC
## PHILIPPINES 0.1168 [ 0.1167; 0.1169] 0.1 1.6 LMIC
## POLAND 0.7640 [ 0.7635; 0.7645] 0.3 1.6 HIC
## PORTUGAL 4.8934 [ 4.8911; 4.8956] 0.6 1.6 HIC
## PUERTO RICO 13.9333 [13.9267; 13.9400] 0.5 1.6 HIC
## ROMANIA 1.2775 [ 1.2767; 1.2783] 0.3 1.6 UMIC
## RUSSIA 0.2668 [ 0.2667; 0.2670] 0.4 1.6 UMIC
## SAUDI ARABIA 1.3045 [ 1.3039; 1.3052] 0.5 1.6 HIC
## SERBIA 1.1215 [ 1.1203; 1.1226] 0.1 1.6 UMIC
## SLOVAKIA 5.0019 [ 4.9988; 5.0050] 0.3 1.6 HIC
## SLOVENIA 3.6342 [ 3.6299; 3.6385] 0.1 1.6 HIC
## SOUTH AFRICA 0.3238 [ 0.3236; 0.3241] 0.2 1.6 UMIC
## SOUTH KOREA 2.4374 [ 2.4367; 2.4381] 1.4 1.6 HIC
## SPAIN 6.6803 [ 6.6791; 6.6816] 3.5 1.6 HIC
## SWEDEN 6.5158 [ 6.5132; 6.5185] 0.7 1.6 HIC
## SWITZERLAND 3.6917 [ 3.6895; 3.6938] 0.3 1.6 HIC
## TUNISIA 0.6111 [ 0.6103; 0.6118] 0.1 1.6 LMIC
## TÜRKIYE 5.0061 [ 5.0053; 5.0069] 4.5 1.6 UMIC
## UNITED ARAB EMIRATES 0.7763 [ 0.7753; 0.7772] 0.1 1.6 HIC
## UNITED KINGDOM 12.1779 [12.1766; 12.1793] 9.0 1.6 HIC
## UNITED STATES 11.2986 [11.2980; 11.2992] 40.7 1.6 HIC
## URUGUAY 1.5550 [ 1.5528; 1.5572] 0.1 1.6 HIC
## VENEZUELA 0.6046 [ 0.6041; 0.6051] 0.2 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 5.5252 [5.5250; 5.5253] 97843.79 0
## Random effects model 1.3957 [1.0368; 1.8789] 2.20 0.0279
##
## Quantifying heterogeneity:
## tau^2 = 1.4493 [1.1160; 3.0809]; tau = 1.2039 [1.0564; 1.7552]
## I^2 = 100.0%; H = 7882.65
##
## Test of heterogeneity:
## Q d.f. p-value
## 3852441325.90 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 8.1086 [8.1082; 8.1089] 836552647.67 100.0%
## income = UMIC 20 1.6761 [1.6759; 1.6763] 464720616.29 100.0%
## income = LMIC 6 0.4822 [0.4822; 0.4823] 69120852.87 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2482047209.07 2 0
## Within groups 1370394116.83 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 3.4407 [2.7898; 4.2436] 0.4237 0.6509
## income = UMIC 20 0.4034 [0.2122; 0.7669] 2.1487 1.4659
## income = LMIC 6 0.3350 [0.1846; 0.6081] 0.5552 0.7451
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 82.45 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 3.0485 [ 3.0476; 3.0494] 1.3 1.6 UMIC
## ARGENTINA 1.3337 [ 1.3331; 1.3342] 0.6 1.6 UMIC
## AUSTRALIA 8.9037 [ 8.9017; 8.9056] 2.2 1.6 HIC
## AUSTRIA 5.5051 [ 5.5026; 5.5077] 0.5 1.6 HIC
## BELARUS 0.1178 [ 0.1174; 0.1181] 0.0 1.6 UMIC
## BELGIUM 5.0984 [ 5.0963; 5.1006] 0.6 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.3756 [ 0.3746; 0.3767] 0.0 1.6 UMIC
## BRAZIL 0.5157 [ 0.5155; 0.5158] 1.1 1.6 UMIC
## BULGARIA 1.2027 [ 1.2013; 1.2040] 0.1 1.6 UMIC
## CANADA 10.4997 [10.4979; 10.5014] 3.9 1.6 HIC
## CHILE 0.8795 [ 0.8788; 0.8802] 0.2 1.6 HIC
## CHINA 0.0055 [ 0.0054; 0.0055] 0.1 1.6 UMIC
## COLOMBIA 0.1728 [ 0.1727; 0.1730] 0.1 1.6 UMIC
## CROATIA 1.1773 [ 1.1756; 1.1790] 0.0 1.6 HIC
## CZECH REPUBLIC 4.2816 [ 4.2796; 4.2837] 0.5 1.6 HIC
## ECUADOR 0.5272 [ 0.5266; 0.5277] 0.1 1.6 UMIC
## EGYPT 2.0563 [ 2.0558; 2.0568] 2.0 1.6 LMIC
## ESTONIA 2.2727 [ 2.2684; 2.2769] 0.0 1.6 HIC
## FINLAND 7.4485 [ 7.4448; 7.4523] 0.4 1.6 HIC
## FRANCE 5.5702 [ 5.5692; 5.5711] 3.6 1.6 HIC
## GERMANY 5.1636 [ 5.1627; 5.1644] 4.3 1.6 HIC
## GREECE 4.2748 [ 4.2727; 4.2768] 0.5 1.6 HIC
## HUNGARY 2.0103 [ 2.0088; 2.0117] 0.2 1.6 HIC
## INDIA 0.4575 [ 0.4575; 0.4576] 6.1 1.6 LMIC
## IRELAND 9.8757 [ 9.8710; 9.8803] 0.5 1.6 HIC
## ITALY 2.5542 [ 2.5536; 2.5549] 1.6 1.6 HIC
## JAPAN 2.6631 [ 2.6626; 2.6636] 3.4 1.6 HIC
## JORDAN 1.1300 [ 1.1289; 1.1311] 0.1 1.6 UMIC
## KAZAKHSTAN 0.0869 [ 0.0867; 0.0871] 0.0 1.6 UMIC
## KUWAIT 2.6181 [ 2.6155; 2.6207] 0.1 1.6 HIC
## LATVIA 2.1287 [ 2.1253; 2.1320] 0.0 1.6 HIC
## LEBANON 1.4841 [ 1.4826; 1.4856] 0.1 1.6 UMIC
## LITHUANIA 1.6811 [ 1.6786; 1.6836] 0.0 1.6 HIC
## LUXEMBOURG 4.4402 [ 4.4314; 4.4491] 0.0 1.6 HIC
## MEXICO 0.3813 [ 0.3811; 0.3815] 0.5 1.6 UMIC
## MOROCCO 0.1463 [ 0.1461; 0.1465] 0.1 1.6 LMIC
## NETHERLANDS 3.7637 [ 3.7622; 3.7653] 0.6 1.6 HIC
## NEW ZEALAND 3.8068 [ 3.8038; 3.8097] 0.2 1.6 HIC
## NORWAY 6.2148 [ 6.2112; 6.2183] 0.3 1.6 HIC
## PAKISTAN 0.3403 [ 0.3402; 0.3405] 0.7 1.6 LMIC
## PERU 0.2237 [ 0.2234; 0.2239] 0.1 1.6 UMIC
## PHILIPPINES 0.1269 [ 0.1268; 0.1271] 0.1 1.6 LMIC
## POLAND 1.1753 [ 1.1747; 1.1759] 0.4 1.6 HIC
## PORTUGAL 4.9566 [ 4.9543; 4.9588] 0.5 1.6 HIC
## PUERTO RICO 15.5856 [15.5784; 15.5928] 0.5 1.6 HIC
## ROMANIA 1.5114 [ 1.5105; 1.5123] 0.3 1.6 UMIC
## RUSSIA 0.3643 [ 0.3641; 0.3645] 0.5 1.6 UMIC
## SAUDI ARABIA 1.8736 [ 1.8729; 1.8744] 0.6 1.6 HIC
## SERBIA 1.4789 [ 1.4775; 1.4802] 0.1 1.6 UMIC
## SLOVAKIA 4.6831 [ 4.6801; 4.6861] 0.3 1.6 HIC
## SLOVENIA 3.8317 [ 3.8273; 3.8361] 0.1 1.6 HIC
## SOUTH AFRICA 0.3350 [ 0.3347; 0.3352] 0.2 1.6 UMIC
## SOUTH KOREA 2.7612 [ 2.7604; 2.7619] 1.4 1.6 HIC
## SPAIN 6.9574 [ 6.9561; 6.9586] 3.3 1.6 HIC
## SWEDEN 7.0097 [ 7.0070; 7.0125] 0.7 1.6 HIC
## SWITZERLAND 3.7831 [ 3.7809; 3.7852] 0.3 1.6 HIC
## TUNISIA 0.8180 [ 0.8172; 0.8189] 0.1 1.6 LMIC
## TÜRKIYE 5.8332 [ 5.8324; 5.8341] 4.7 1.6 UMIC
## UNITED ARAB EMIRATES 0.6780 [ 0.6771; 0.6789] 0.1 1.6 HIC
## UNITED KINGDOM 13.1573 [13.1559; 13.1588] 8.8 1.6 HIC
## UNITED STATES 12.3396 [12.3390; 12.3402] 40.2 1.6 HIC
## URUGUAY 1.7113 [ 1.7090; 1.7135] 0.1 1.6 HIC
## VENEZUELA 0.4621 [ 0.4617; 0.4625] 0.1 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 5.9393 [5.9391; 5.9395] 107558.53 0
## Random effects model 1.6048 [1.1959; 2.1535] 3.15 0.0016
##
## Quantifying heterogeneity:
## tau^2 = 1.4185 [1.0737; 2.9414]; tau = 1.1910 [1.0362; 1.7151]
## I^2 = 100.0%; H = 8247.35
##
## Test of heterogeneity:
## Q d.f. p-value
## 4217162820.97 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 8.7568 [8.7565; 8.7572] 925321899.03 100.0%
## income = UMIC 20 2.0763 [2.0761; 2.0765] 567163087.91 100.0%
## income = LMIC 6 0.6086 [0.6085; 0.6087] 150455996.90 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2574221837.13 2 0
## Within groups 1642940983.84 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 3.8232 [3.0939; 4.7245] 0.4316 0.6569
## income = UMIC 20 0.4844 [0.2575; 0.9114] 2.0795 1.4420
## income = LMIC 6 0.4116 [0.1911; 0.8867] 0.9196 0.9590
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 61.83 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 4
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 2.9435 [ 2.9427; 2.9444] 1.2 1.6 UMIC
## ARGENTINA 1.3902 [ 1.3896; 1.3907] 0.6 1.6 UMIC
## AUSTRALIA 9.0369 [ 9.0350; 9.0389] 2.1 1.6 HIC
## AUSTRIA 5.7985 [ 5.7959; 5.8011] 0.5 1.6 HIC
## BELARUS 0.1786 [ 0.1781; 0.1790] 0.0 1.6 UMIC
## BELGIUM 5.4506 [ 5.4484; 5.4529] 0.6 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.5333 [ 0.5320; 0.5346] 0.0 1.6 UMIC
## BRAZIL 0.6318 [ 0.6316; 0.6320] 1.2 1.6 UMIC
## BULGARIA 1.3621 [ 1.3607; 1.3636] 0.1 1.6 UMIC
## CANADA 10.6320 [10.6303; 10.6338] 3.7 1.6 HIC
## CHILE 0.9280 [ 0.9273; 0.9288] 0.2 1.6 HIC
## CHINA 0.0067 [ 0.0067; 0.0067] 0.1 1.6 UMIC
## COLOMBIA 0.1955 [ 0.1953; 0.1957] 0.1 1.6 UMIC
## CROATIA 1.3364 [ 1.3346; 1.3383] 0.1 1.6 HIC
## CZECH REPUBLIC 4.6664 [ 4.6642; 4.6685] 0.5 1.6 HIC
## ECUADOR 0.5475 [ 0.5469; 0.5481] 0.1 1.6 UMIC
## EGYPT 3.9544 [ 3.9538; 3.9551] 3.7 1.6 LMIC
## ESTONIA 3.0000 [ 2.9952; 3.0049] 0.0 1.6 HIC
## FINLAND 7.9960 [ 7.9921; 7.9999] 0.4 1.6 HIC
## FRANCE 5.8131 [ 5.8121; 5.8141] 3.6 1.6 HIC
## GERMANY 5.3900 [ 5.3892; 5.3908] 4.2 1.6 HIC
## GREECE 4.6340 [ 4.6318; 4.6361] 0.5 1.6 HIC
## HUNGARY 2.1400 [ 2.1385; 2.1415] 0.2 1.6 HIC
## INDIA 0.5129 [ 0.5129; 0.5130] 6.5 1.6 LMIC
## IRELAND 8.2664 [ 8.2621; 8.2706] 0.4 1.6 HIC
## ITALY 2.6081 [ 2.6074; 2.6088] 1.5 1.6 HIC
## JAPAN 2.8603 [ 2.8598; 2.8608] 3.4 1.6 HIC
## JORDAN 0.6513 [ 0.6504; 0.6521] 0.1 1.6 UMIC
## KAZAKHSTAN 0.1014 [ 0.1011; 0.1016] 0.0 1.6 UMIC
## KUWAIT 3.2250 [ 3.2222; 3.2279] 0.1 1.6 HIC
## LATVIA 2.5702 [ 2.5665; 2.5740] 0.0 1.6 HIC
## LEBANON 1.5687 [ 1.5672; 1.5703] 0.1 1.6 UMIC
## LITHUANIA 1.9779 [ 1.9751; 1.9806] 0.1 1.6 HIC
## LUXEMBOURG 4.4238 [ 4.4151; 4.4326] 0.0 1.6 HIC
## MEXICO 0.4126 [ 0.4124; 0.4128] 0.5 1.6 UMIC
## MOROCCO 0.2011 [ 0.2009; 0.2014] 0.1 1.6 LMIC
## NETHERLANDS 3.9523 [ 3.9507; 3.9538] 0.6 1.6 HIC
## NEW ZEALAND 4.9473 [ 4.9440; 4.9507] 0.2 1.6 HIC
## NORWAY 6.5230 [ 6.5194; 6.5266] 0.3 1.6 HIC
## PAKISTAN 0.3993 [ 0.3992; 0.3995] 0.8 1.6 LMIC
## PERU 0.2562 [ 0.2559; 0.2565] 0.1 1.6 UMIC
## PHILIPPINES 0.1533 [ 0.1532; 0.1535] 0.2 1.6 LMIC
## POLAND 1.6393 [ 1.6386; 1.6400] 0.6 1.6 HIC
## PORTUGAL 5.1168 [ 5.1145; 5.1191] 0.5 1.6 HIC
## PUERTO RICO 14.7318 [14.7247; 14.7390] 0.4 1.6 HIC
## ROMANIA 1.6071 [ 1.6061; 1.6080] 0.3 1.6 UMIC
## RUSSIA 0.4930 [ 0.4928; 0.4932] 0.7 1.6 UMIC
## SAUDI ARABIA 1.1357 [ 1.1351; 1.1363] 0.4 1.6 HIC
## SERBIA 1.9559 [ 1.9543; 1.9574] 0.2 1.6 UMIC
## SLOVAKIA 5.1169 [ 5.1138; 5.1201] 0.3 1.6 HIC
## SLOVENIA 3.9999 [ 3.9954; 4.0044] 0.1 1.6 HIC
## SOUTH AFRICA 0.3738 [ 0.3736; 0.3741] 0.2 1.6 UMIC
## SOUTH KOREA 3.0995 [ 3.0987; 3.1003] 1.5 1.6 HIC
## SPAIN 7.1761 [ 7.1748; 7.1773] 3.2 1.6 HIC
## SWEDEN 7.4427 [ 7.4399; 7.4455] 0.7 1.6 HIC
## SWITZERLAND 3.9074 [ 3.9052; 3.9096] 0.3 1.6 HIC
## TUNISIA 2.0132 [ 2.0119; 2.0146] 0.2 1.6 LMIC
## TÜRKIYE 6.0801 [ 6.0792; 6.0809] 4.7 1.6 UMIC
## UNITED ARAB EMIRATES 0.6640 [ 0.6631; 0.6648] 0.1 1.6 HIC
## UNITED KINGDOM 13.4831 [13.4816; 13.4845] 8.5 1.6 HIC
## UNITED STATES 12.5402 [12.5396; 12.5409] 38.6 1.6 HIC
## URUGUAY 1.5402 [ 1.5380; 1.5424] 0.0 1.6 HIC
## VENEZUELA 0.4182 [ 0.4178; 0.4186] 0.1 1.6 UMIC
##
## Number of studies combined: k = 63
##
## rate 95%-CI z p-value
## Common effect model 5.9717 [5.9716; 5.9719] 111331.67 0
## Random effects model 1.7730 [1.3306; 2.3625] 3.91 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.3512 [1.0190; 2.7705]; tau = 1.1624 [1.0095; 1.6645]
## I^2 = 100.0%; H = 8366.72
##
## Test of heterogeneity:
## Q d.f. p-value
## 4340120531.46 62 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 8.9293 [8.9290; 8.9297] 916989278.64 100.0%
## income = UMIC 20 2.0996 [2.0994; 2.0998] 596650391.49 100.0%
## income = LMIC 6 0.9741 [0.9740; 0.9742] 439396798.75 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2387084062.58 2 0
## Within groups 1953036468.88 60 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 4.0260 [3.2736; 4.9513] 0.4123 0.6421
## income = UMIC 20 0.5362 [0.2891; 0.9947] 1.9875 1.4098
## income = LMIC 6 0.6075 [0.2107; 1.7515] 1.7511 1.3233
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 46.21 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
##income
gaba_income<-as.data.frame(do.call(rbind, datout))[c(5:10)]
gaba_income$Year<-as.numeric(gaba_income$Year)
out <- unlist(gaba_income)
Year<-out[1:44]
Drug<-out[45:88]
DDDTID<-out[89:220]
lower<-out[221:352]
upper<-out[353:484]
out2<-data.frame(Year,Drug)
out3<-do.call("rbind", replicate(3, out2, simplify = FALSE))
newdata <- out3%>% arrange(Drug, Year)
newdata$DDDTID<-DDDTID
newdata$lower<-lower
newdata$upper<-upper
region_name<-(rep(c("HIC","UMIC","LMIC"),44))
newdata$Income<-region_name
newdata$Drug<-c(rep(("Gabapentin"), 33),
rep(("Gabapentin Enacarbil"), 33),
rep(("Pregabalin"), 33),
rep(("Gabapentinoids"), 33))
gaba_income2<-newdata
gaba_income2$`DDD/TID`<-gaba_income2$DDDTID
gaba_income2$`DDD/TID - lower`<-gaba_income2$lower
gaba_income2$`DDD/TID - upper`<-gaba_income2$upper
gaba_income3<-gaba_income2[c(1,2,6:9)]
library(DT)
datatable(gaba_income3, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
write.csv(gaba_income3,"D:/R/midas gaba/R1/Output_12_Sens2_Subgroup_AAPC_income_meta.csv")
Sensitivity analysis 3: without imputed data
Reading data
Data inclusion criteria: variable name “MOL”= “PREGABALIN”,“GABAPENTIN”,“GABAPENTIN ENACARBIL”,“MIROGABALIN”
library(readxl)
library(dplyr)
# data <- read.csv("D:/MIDAS ADHD/midas_adhd/adhd/MIDAS 2019.csv")
# data
getwd()
## [1] "D:/R/midas gaba"
setwd("D:/R/midas gaba")
gaba<-subset(read.csv("D:/R/midas benzo/raw/gaba_extract.csv"),MOL=="PREGABALIN"|
MOL=="GABAPENTIN"|
MOL=="GABAPENTIN ENACARBIL"|
MOL=="MIROGABALIN")
gaba$Class<-"Gabapentinoids"
Keep SU and DDD data
master<-gaba
Separate data into with DDD units and without DDD units (INTWHODDDDESC NOT ASSIGNED)
detach(package:plyr)
library(dplyr)
#Identify products that need conversion from SU to INTDDD
missing_DDD<-subset(master,INTWHODDDDESC=="INTWHODDD DESC NOT ASSIGNED")
with_DDD<-subset(master,INTWHODDDDESC!="INTWHODDD DESC NOT ASSIGNED")
# #Export list for conversion factor identification
# miss_DDD<-unique(missing_DDD[c(1:8)])
# write.csv(miss_DDD,"D:/R/midas benzo/dosage_convert.csv")
#Daniel and Andrew has done the conversion
convert <- read.table(
"D:/R/midas benzo/convert_20221114_removed.txt",
sep="\t", header=TRUE)
convert2<-subset(convert, !is.na(convert$Strength.of.BZD.or.GABA) & (MOL=="PREGABALIN"|
MOL=="GABAPENTIN"|
MOL=="GABAPENTIN ENACARBIL"|
MOL=="MIROGABALIN"))
convert_missing<-subset(convert, is.na(convert$Strength.of.BZD.or.GABA))
convert_medianimpute_strength <- convert2[c(1:10)] %>% group_by (CTY,SEC, MNF, ATC3,INTPRD,NFC123,INTWHODDDDESC,MOL,INTWHODDDDESC_2) %>% mutate(median=median(Strength.of.BZD.or.GABA, na.rm = TRUE))
convert_medianimpute_strength2<-unique(convert_medianimpute_strength[c(1:8,10,11)])
#left join convert to data
missing_DDD_convert <- left_join(missing_DDD,convert_medianimpute_strength2,by = c("CTY","SEC","MNF","ATC3","INTPRD","NFC123",
"INTWHODDDDESC","MOL") )
write.csv(missing_DDD,"D:/R/midas gaba/withoutddd_539.csv")
missing_DDD_remain<-subset(missing_DDD_convert, !is.na(median) & Class=="Gabapentinoids")
missing_DDD_agg<-missing_DDD %>% group_by (CTY, SEC,MNF,ATC3,INTPRD,NFC123,INTWHODDDDESC,MOL,X_TYPE_,X_FREQ_,Class) %>%
summarise_at(vars(1:857),sum)
Aggregate separately for DDD and missing DDD dataframes
DDD
library(tidyr)
library(zoo)
library(dplyr)
ddd <-with_DDD
## Remove columns of other sales data e.g. LC, LCD, USD, CU, SU
ddd.2 <- ddd[-c(9:10)]%>%select(-contains(c("LC_MNF","LCD_MNF","USD_MNF","CU_MTH","SU_MTH")))
ddd.3 <- pivot_longer(ddd.2, cols=9:151,
names_to = "Month", values_to = "DDD")
## Format date
ddd.3$Month<-str_remove(ddd.3$Month, "INTDDD_MTH_")
ddd.3<-ddd.3 %>% separate(Month, c("Month","Year"),
sep = "([_])")
ddd.3$Year <- paste0("20", ddd.3$Year, sep = "")
ddd.3$Year<-as.numeric(ddd.3$Year)
ddd.3$Month<-as.numeric(ddd.3$Month)
ddd.3$Date <- as.yearmon(paste(ddd.3$Year,
ddd.3$Month), "%Y %m")
ddd.4<-ddd.3[-c(10)]
ddd.4$DDD[is.na(ddd.4$DDD)]<-0
Aggregate the consumption data by country, drug name, date
ddd.5<-ddd.4
bind<-(ddd.5)
aggregate<-bind[-c(2:7)]
#Step 1. Clean city names
aggregate$CTY = sub(pattern = "(RETAIL)|(COMBINED)|(COMBINE)|(COMBIN)|(COMBI)|(RET)|(R.MUTUALES)|(HOSPITAL)|(TOTAL SALES)",
replacement = "", x = aggregate$CTY, perl = TRUE)
aggregate.2<-aggregate %>% group_by (CTY, MOL,Class,Year, Date) %>%
summarise_at(vars(1),sum, na.rm = TRUE)
Merge rx data with UN population data
We excluded data where Country==“FRENCH WEST AFRICA” or Country==“CENTRAL AMERICA” as national level data was not available. The included countries/regions were divided into the following areas: Northern America, Central and South America, Northern Europe, Eastern Europe, Southern Europe, Western Europe, Oceania, Central Asia, Eastern Asia, South-eastern Asia, Southern Asia, Western Asia, Northern Africa, and Southern Africa, based on their geographical regions according to United Nations (UN)’ “Standard Country or Area Codes for Statistical Use”.
library(readxl)
library(dplyr)
#population size from UN====
pop <- read_excel(path = "D:/R/midas adhd/ref data/WPP2019_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx",
sheet = "ESTIMATES",
range = "B17:DE18122")
pop.2<-subset(pop, Type=="Country/Area")
pop.4<-pop.2[-c(1,3:63,77:108)]
names(pop.4)[names(pop.4) == 'Region, subregion, country or area *'] <- 'Country'
pop.5 <- pivot_longer(pop.4, cols=2:14,
names_to = "Year", values_to = "Population")
pop.5$Country <- toupper(pop.5$Country)
names(aggregate.2)[names(aggregate.2) == 'CTY'] <- 'Country'
aggregate.2$Country <-trimws(aggregate.2$Country)
'%ni%' <- Negate('%in%')
ddd.set.2<- subset(aggregate.2, !Country=="C. AMERICA"&
!Country=="FR. W. AFRICA"&
!Country=="FRENCH WEST AFRICA"&
!Country=="CENTRAL AMERICA")
pop.7<-pop.5
rename <- pop.7 %>%filter(Country %ni% ddd.set.2$Country)
rename<-unique(rename$Country)
ddd.set.2[ddd.set.2$Country == "CZECH", "Country"] <- "CZECH REPUBLIC"
ddd.set.2[ddd.set.2$Country == "NETHERLNDS", "Country"] <- "NETHERLANDS"
ddd.set.2[ddd.set.2$Country == "RUSSIAN FED.", "Country"] <- "RUSSIA"
ddd.set.2[ddd.set.2$Country == "TURKEY", "Country"] <- "TÜRKİYE"
ddd.set.2[ddd.set.2$Country == "UAE", "Country"] <- "UNITED ARAB EMIRATES"
ddd.set.2[ddd.set.2$Country == "UK", "Country"] <- "UNITED KINGDOM"
ddd.set.2[ddd.set.2$Country == "USA", "Country"] <- "UNITED STATES"
ddd.set.2[ddd.set.2$Country == "US", "Country"] <- "UNITED STATES"
ddd.set.2[ddd.set.2$Country == "S. AFRICA", "Country"] <- "SOUTH AFRICA"
ddd.set.2[ddd.set.2$Country == "BOSNIA", "Country"] <- "BOSNIA AND HERZEGOVINA"
ddd.set.2[ddd.set.2$Country == "KOREA", "Country"] <- "SOUTH KOREA"
pop.7<-pop.5
pop.7[pop.7$Country == "CZECHIA", "Country"] <- "CZECH REPUBLIC"
pop.7[pop.7$Country == "REPUBLIC OF KOREA", "Country"] <- "SOUTH KOREA"
pop.7[pop.7$Country == "RUSSIAN FEDERATION", "Country"] <- "RUSSIA"
pop.7[pop.7$Country == "TURKEY", "Country"] <- "TÜRKİYE"
pop.7[pop.7$Country == "CHINA, TAIWAN PROVINCE OF CHINA", "Country"] <- "TAIWAN"
pop.7[pop.7$Country == "VENEZUELA (BOLIVARIAN REPUBLIC OF)", "Country"] <- "VENEZUELA"
pop.7[pop.7$Country == "UNITED STATES OF AMERICA", "Country"] <- "UNITED STATES"
pop.8<-as.data.frame(pop.7)
ddd.set.2$Year<-as.numeric(ddd.set.2$Year)
pop.8$Year<-as.numeric(pop.8$Year)
ddd.set.3.1<-ddd.set.2
ddd.set.3 <- left_join(ddd.set.3.1,pop.8,by = c("Country","Year") )
## Merge data with Region categorisation====
Region <- read_excel(path = "D:/R/midas adhd/ref data/UNSD — Methodology.xlsx")
Region.2<-Region[c(6,9,16)]
names(Region.2)[names(Region.2) == 'Country or Area'] <- 'Country'
Region.2$Country <- toupper(Region.2$Country)
rename <- ddd.set.3%>%filter(Country %ni% Region.2$Country)
rename<-unique(rename$Country)
Region.2[Region.2$Country == "CZECHIA", "Country"] <- "CZECH REPUBLIC"
Region.2[Region.2$Country == "REPUBLIC OF KOREA", "Country"] <- "SOUTH KOREA"
Region.2[Region.2$Country == "RUSSIAN FEDERATION", "Country"] <- "RUSSIA"
Region.2[Region.2$Country == "CHINA, TAIWAN PROVINCE OF CHINA", "Country"] <- "TAIWAN"
Region.2[Region.2$Country == "TURKEY", "Country"] <- "TÜRKİYE"
Region.2[Region.2$Country == "VENEZUELA (BOLIVARIAN REPUBLIC OF)", "Country"] <- "VENEZUELA"
Region.2[Region.2$Country == "UNITED STATES OF AMERICA", "Country"] <- "UNITED STATES"
Region.2[Region.2$Country == "UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND", "Country"] <- "UNITED KINGDOM"
Region.3<-Region.2%>%filter(Country %in% ddd.set.3$Country)
ddd.set.4 <- left_join(ddd.set.3,Region.3,by = c("Country") )
names(ddd.set.4)[names(ddd.set.4) == 'Sub-region Name'] <- 'Region'
ddd.set.4[ddd.set.4$Country == "TAIWAN", "Region"] <- "Eastern Asia"
ddd.set.5<-ddd.set.4[-c(9)]
## 1.9 Dave comment: change Latin America
ddd.set.5[ddd.set.5$Region == "Latin America and the Caribbean", "Region"] <- "Central and South America and the Caribbean"
ddd.set.5[ddd.set.5$Region == "Sub-Saharan Africa", "Region"] <- "Southern Africa"
population<-pop.8
population$Population<-as.numeric(population$Population)
options(scipen=999)
Final analytic dataset
- Data from 2007 and 2019 were removed due to missing months
- DDD data were aggregated by Drug, Country, Year
- DDD data were combined with population data
- zeros were added to countries were there were no record in that year
- Outcome metric = DDD per thousand population per day (DDD/(population in thousand*365.25))
- Final cleaned analytic dataset is cleaned as below with variables specification:
library(dplyr)
cty.all<-ddd.set.5
names(cty.all)[names(cty.all) == 'MOL'] <- 'Drug'
## create one df of monthly data just in case
cty.all.3<-cty.all[-c(7)] %>% group_by (Year,Date,Class,Drug, Country, Region) %>%
summarise_at(vars(1),sum, na.rm = TRUE)
## numerator
cty.ddd.2<-cty.all[-c(5,7)] %>% group_by (Year,Class,Drug, Country, Region) %>%
summarise_at(vars(1),sum, na.rm = TRUE)
##denominator
cty.pop<-population
#Aggregate benzos and gaba
#gaba
new_gaba<-subset(cty.ddd.2)
new_gaba<-new_gaba[-c(3)]
new_gaba<-new_gaba %>% group_by (Year,Country, Region,Class) %>%
summarise_at(vars(1),sum, na.rm = TRUE)
new_gaba$Drug<-"Gabapentinoids"
new_gaba$DDD[is.na(new_gaba$DDD)]<-0
cty.ddd.2<-rbind(cty.ddd.2,new_gaba)
#Add zeros
Drug<-sort(rep(c("Gabapentinoids","PREGABALIN","GABAPENTIN"),715))
Country<-(rep(unique(cty.ddd.2$Country),11))
Year<-sort(rep(c(2008:2018),65))
merge_zero <- data.frame(Country, Year)
merge_zero2<-do.call("rbind", replicate(3, merge_zero, simplify = FALSE))
merge_zero2$Drug<-Drug
cty.ddd.3 <- right_join(x=cty.ddd.2,y=merge_zero2,
by=c("Year","Country","Drug"))
cty.ddd.4 <- left_join(x=cty.ddd.3,y=cty.pop,
by=c("Year","Country"))
cty.ddd.5 <- distinct(left_join(x=cty.ddd.4[-c(2,5)],y=ddd.set.5[c(1,3,8)],
by=c("Country")))
cty.ddd.5$DDD[is.na(cty.ddd.5$DDD)]<-0
#Divide
cty.ddd.5$DDDPTPD <-(cty.ddd.5$DDD/cty.ddd.5$Population)/365.25
cty.ddd.5$Drug<-str_to_title(cty.ddd.5$Drug)
analy<-subset(cty.ddd.5,Year!=2007 & Year!=2019 & Drug!="Vigbatrin"&Drug!="Mirogabalin")
library(DT)
datatable(analy, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
str(analy,give.attr=FALSE)
## grouped_df [2,145 x 8] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Year : num [1:2145] 2008 2008 2008 2008 2008 ...
## $ Drug : chr [1:2145] "Gabapentin" "Gabapentin" "Gabapentin" "Gabapentin" ...
## $ Country : chr [1:2145] "ALGERIA" "ARGENTINA" "AUSTRALIA" "AUSTRIA" ...
## $ DDD : num [1:2145] 0 1532866 5911624 4399525 16780 ...
## $ Population: num [1:2145] 34731 40080 21332 8342 9453 ...
## $ Class : chr [1:2145] "Gabapentinoids" "Gabapentinoids" "Gabapentinoids" "Gabapentinoids" ...
## $ Region : chr [1:2145] "Northern Africa" "Central and South America and the Caribbean" "Australia and New Zealand" "Western Europe" ...
## $ DDDPTPD : num [1:2145] 0 0.10471 0.75872 1.44401 0.00486 ...
Main Analysis: DDD/TID and trends
Overview of gabapentinoids consumption by Drug, Country, and Year
*MIROGABALIN is only available in Japan in 2019 which is not covered in our analysis
library(ggplot2)
library(RColorBrewer)
new_set.2<-analy
Calculate CIs of consumption in DDD/TID per year
The function pois.approx from R package epitools is used to calculation the Confidence intervals for Poisson rates with normal approximation
library(epitools)
CIs<-pois.approx(new_set.2$DDD, pt = new_set.2$Population*365.25, conf.level = 0.95)
pool.new_set<-cbind(new_set.2,CIs)
pool.new_set.2<-subset(pool.new_set)
pool.new_set.2$DDD<-round(pool.new_set.2$DDD, digits = 5)
Meta-analysis of DDD/TID by Year
When DDD=0 or extremely small, Country data wont be counted as number of studies in the meta analysis
Drug indices: [1] “Gabapentin”, [2] “Gabapentin Enacarbil”, [3] “Pregabalin”, [4]“Gabapentinoids”
options(width=800)
library(Rcpp)
library(meta)
library(data.table)
library(dplyr)
set_subzero<-subset(analy, Drug!="MIROGABALIN")
set_subzero$DDD_dum=set_subzero$DDD
set_subzero$DDD_dum[set_subzero$DDD==0]<-0.00001
CIs<-pois.approx(set_subzero$DDD_dum, set_subzero$Population*365.25, conf.level = 0.95)
meta.gaba<-cbind(set_subzero,CIs)
meta.gaba$Region <- factor(meta.gaba$Region, levels =
c("Northern America","Central and South America and the Caribbean",
"Northern Europe", "Eastern Europe","Southern Europe", "Western Europe",
"Australia and New Zealand" ,
"Eastern Asia" , "Central Asia",
"South-eastern Asia","Southern Asia" ,
"Western Asia",
"Northern Africa","Southern Africa"
))
meta <- function(rho, iseed){
meta.gaba_1<- subset(meta.gaba, Year==rho & Drug==iseed)
m1_var<-metagen(log(meta.gaba_1$rate),
lower = log(meta.gaba_1$lower),
upper = log(meta.gaba_1$upper),
studlab = meta.gaba_1$Country,
sm = "IRLN", method.tau = "DL",
comb.fixed = TRUE,
byvar = meta.gaba_1$Region)
print(c(rho, iseed))
print(summary(m1_var), digits=4)
est.random<-c("Year", "DDD/TID", "DDD/TID - lower","DDD/TID - upper")
est.random$Year<-rho
est.random$Drug<-iseed
est.random$`DDD/TID`<-exp(summary(m1_var)$TE.random)
est.random$`DDD/TID - lower`<-exp(summary(m1_var)$lower.random)
est.random$`DDD/TID - upper`<-exp(summary(m1_var)$upper.random)
est.by.random<-c("Year", "DDD/TID", "DDD/TID - lower","DDD/TID - upper")
est.by.random$Year<-rho
est.by.random$Drug<-iseed
est.by.random$`DDD/TID`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$TE)))))
est.by.random$`DDD/TID - lower`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$lower)))))
est.by.random$`DDD/TID - upper`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$upper)))))
return(c(est.random,est.by.random))
}
datin <- expand.grid(rho = unique(meta.gaba$Year), iseed = unique(meta.gaba$Drug))
i <- 1:nrow(datin)
datout <- with(datin,
lapply(i, function(j){meta(rho[j], iseed[j])}))
## [1] 2008 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1047 [0.1045; 0.1049] 0.2 1.7 Central and South America and the Caribbean
## AUSTRALIA 0.7587 [0.7581; 0.7593] 0.7 1.7 Australia and New Zealand
## AUSTRIA 1.4440 [1.4427; 1.4454] 0.6 1.7 Western Europe
## BELARUS 0.0049 [0.0048; 0.0049] 0.0 1.7 Eastern Europe
## BELGIUM 0.3490 [0.3484; 0.3495] 0.2 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0522 [0.0522; 0.0523] 0.5 1.7 Central and South America and the Caribbean
## BULGARIA 0.0867 [0.0863; 0.0870] 0.0 1.7 Eastern Europe
## CANADA 2.4878 [2.4870; 2.4887] 3.8 1.7 Northern America
## CHILE 0.0425 [0.0423; 0.0426] 0.0 1.7 Central and South America and the Caribbean
## CHINA 0.0009 [0.0009; 0.0009] 0.1 1.7 Eastern Asia
## COLOMBIA 0.0340 [0.0339; 0.0341] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.2926 [0.2918; 0.2935] 0.1 1.7 Southern Europe
## CZECH REPUBLIC 0.7153 [0.7145; 0.7162] 0.3 1.7 Eastern Europe
## ECUADOR 0.0727 [0.0725; 0.0730] 0.0 1.7 Central and South America and the Caribbean
## EGYPT 0.0759 [0.0758; 0.0760] 0.3 1.7 Northern Africa
## ESTONIA 0.0754 [0.0746; 0.0761] 0.0 1.7 Northern Europe
## FINLAND 0.9593 [0.9579; 0.9607] 0.2 1.7 Northern Europe
## FRANCE 1.3793 [1.3788; 1.3798] 3.9 1.7 Western Europe
## GERMANY 1.2294 [1.2290; 1.2298] 4.6 1.7 Western Europe
## GREECE 0.8335 [0.8326; 0.8344] 0.4 1.7 Southern Europe
## HUNGARY 0.2259 [0.2254; 0.2264] 0.1 1.7 Eastern Europe
## INDIA 0.0096 [0.0096; 0.0096] 0.5 1.7 Southern Asia
## IRELAND 0.9193 [0.9178; 0.9207] 0.2 1.7 Northern Europe
## ITALY 0.5709 [0.5706; 0.5712] 1.5 1.7 Southern Europe
## JAPAN 0.1016 [0.1015; 0.1017] 0.6 1.7 Eastern Asia
## JORDAN 0.0755 [0.0751; 0.0758] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0298 [0.0294; 0.0301] 0.0 1.7 Western Asia
## LATVIA 0.3321 [0.3308; 0.3334] 0.0 1.7 Northern Europe
## LEBANON 0.2416 [0.2409; 0.2424] 0.1 1.7 Western Asia
## LITHUANIA 0.2187 [0.2179; 0.2195] 0.0 1.7 Northern Europe
## LUXEMBOURG 0.8551 [0.8508; 0.8595] 0.0 1.7 Western Europe
## MEXICO 0.0713 [0.0712; 0.0713] 0.4 1.7 Central and South America and the Caribbean
## MOROCCO 0.0074 [0.0073; 0.0074] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.9749 [0.9734; 0.9765] 0.2 1.7 Australia and New Zealand
## NORWAY 1.1055 [1.1040; 1.1071] 0.2 1.7 Northern Europe
## PAKISTAN 0.0542 [0.0542; 0.0543] 0.4 1.7 Southern Asia
## PERU 0.0242 [0.0241; 0.0243] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0260 [0.0260; 0.0261] 0.1 1.7 South-eastern Asia
## POLAND 0.1470 [0.1468; 0.1472] 0.3 1.7 Eastern Europe
## PORTUGAL 1.3098 [1.3087; 1.3109] 0.6 1.7 Southern Europe
## PUERTO RICO 3.7459 [3.7426; 3.7492] 0.6 1.7 Central and South America and the Caribbean
## ROMANIA 0.0597 [0.0595; 0.0599] 0.1 1.7 Eastern Europe
## RUSSIA 0.0152 [0.0152; 0.0152] 0.1 1.7 Eastern Europe
## SAUDI ARABIA 0.1059 [0.1057; 0.1061] 0.1 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.5982 [0.5971; 0.5993] 0.1 1.7 Eastern Europe
## SLOVENIA 0.4548 [0.4533; 0.4564] 0.0 1.7 Southern Europe
## SOUTH AFRICA 0.0515 [0.0514; 0.0516] 0.1 1.7 Southern Africa
## SOUTH KOREA 0.6427 [0.6424; 0.6431] 1.4 1.7 Eastern Asia
## SPAIN 1.7859 [1.7853; 1.7866] 3.8 1.7 Southern Europe
## SWEDEN 1.2074 [1.2063; 1.2086] 0.5 1.7 Northern Europe
## SWITZERLAND 0.6300 [0.6291; 0.6309] 0.2 1.7 Western Europe
## TAIWAN 0.1750 [0.1747; 0.1753] 0.2 1.7 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0646 [0.0643; 0.0648] 0.0 1.7 Northern Africa
## TÜRKIYE 1.1817 [1.1813; 1.1821] 3.8 1.7 Western Asia
## UNITED ARAB EMIRATES 0.1001 [0.0997; 0.1005] 0.0 1.7 Western Asia
## UNITED KINGDOM 1.6870 [1.6864; 1.6875] 4.8 1.7 Northern Europe
## UNITED STATES 4.4760 [4.4756; 4.4764] 62.1 1.7 Northern America
## URUGUAY 0.1957 [0.1949; 0.1964] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.4496 [0.4492; 0.4500] 0.6 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 59
##
## rate 95%-CI z p-value
## Common effect model 2.4562 [2.4561; 2.4564] 25402.37 0
## Random effects model 0.2042 [0.1450; 0.2876] -9.10 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.7993 [1.6079; 5.2238]; tau = 1.3414 [1.2680; 2.2856]
## I^2 = 100.0%; H = 3867.50
##
## Test of heterogeneity:
## Q d.f. p-value
## 867537160.53 58 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 4.3273 [4.3269; 4.3276] 9848134.09 100.0%
## Region = Central and South America and t ... 10 0.2870 [0.2869; 0.2871] 57037395.75 100.0%
## Region = Northern Europe 8 1.5146 [1.5141; 1.5150] 3503305.53 100.0%
## Region = Eastern Europe 8 0.2359 [0.2358; 0.2361] 12165172.49 100.0%
## Region = Southern Europe 6 1.2249 [1.2245; 1.2252] 13237522.89 100.0%
## Region = Western Europe 6 1.2517 [1.2514; 1.2520] 3487176.91 100.0%
## Region = Australia and New Zealand 2 0.7987 [0.7981; 0.7993] 76160.50 100.0%
## Region = Eastern Asia 4 0.3027 [0.3026; 0.3029] 28145086.76 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0260 [0.0260; 0.0261] 0.00 --
## Region = Southern Asia 2 0.0208 [0.0208; 0.0208] 5649936.23 100.0%
## Region = Western Asia 6 1.0335 [1.0331; 1.0339] 9645206.33 100.0%
## Region = Northern Africa 3 0.0691 [0.0691; 0.0692] 445938.84 100.0%
## Region = Southern Africa 1 0.0515 [0.0514; 0.0516] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 724296124.21 12 0
## Within groups 143241036.32 46 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.3370 [1.8767; 5.9336] 0.1725 0.4153
## Region = Central and South America and t ... 10 0.1125 [0.0344; 0.3679] 3.6518 1.9110
## Region = Northern Europe 8 0.5683 [0.4158; 0.7767] 0.2033 0.4509
## Region = Eastern Europe 8 0.0927 [0.0360; 0.2386] 1.8636 1.3651
## Region = Southern Europe 6 0.7274 [0.4290; 1.2333] 0.4354 0.6598
## Region = Western Europe 6 0.8787 [0.7024; 1.0993] 0.0784 0.2799
## Region = Australia and New Zealand 2 0.8601 [0.6727; 1.0996] 0.0314 0.1773
## Region = Eastern Asia 4 0.0572 [0.0107; 0.3072] 2.9416 1.7151
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0260 [0.0260; 0.0261] -- --
## Region = Southern Asia 2 0.0228 [0.0042; 0.1247] 1.5039 1.2263
## Region = Western Asia 6 0.1376 [0.0374; 0.5071] 2.6562 1.6298
## Region = Northern Africa 3 0.0331 [0.0124; 0.0883] 0.7542 0.8685
## Region = Southern Africa 1 0.0515 [0.0514; 0.0516] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 211324.56 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1194 [0.1193; 0.1196] 0.2 1.7 Central and South America and the Caribbean
## AUSTRALIA 0.7770 [0.7764; 0.7776] 0.7 1.7 Australia and New Zealand
## AUSTRIA 1.5762 [1.5748; 1.5776] 0.5 1.7 Western Europe
## BELARUS 0.0093 [0.0092; 0.0094] 0.0 1.7 Eastern Europe
## BELGIUM 0.3669 [0.3663; 0.3675] 0.2 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0590 [0.0590; 0.0591] 0.5 1.7 Central and South America and the Caribbean
## BULGARIA 0.1204 [0.1200; 0.1208] 0.0 1.7 Eastern Europe
## CANADA 2.6364 [2.6355; 2.6373] 3.6 1.7 Northern America
## CHILE 0.0335 [0.0333; 0.0336] 0.0 1.7 Central and South America and the Caribbean
## CHINA 0.0020 [0.0020; 0.0020] 0.1 1.7 Eastern Asia
## COLOMBIA 0.0287 [0.0286; 0.0288] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.2979 [0.2971; 0.2988] 0.1 1.7 Southern Europe
## CZECH REPUBLIC 0.8909 [0.8900; 0.8918] 0.4 1.7 Eastern Europe
## ECUADOR 0.0696 [0.0693; 0.0698] 0.0 1.7 Central and South America and the Caribbean
## EGYPT 0.0833 [0.0832; 0.0834] 0.3 1.7 Northern Africa
## ESTONIA 0.1091 [0.1081; 0.1100] 0.0 1.7 Northern Europe
## FINLAND 0.9637 [0.9623; 0.9650] 0.2 1.7 Northern Europe
## FRANCE 1.2998 [1.2993; 1.3003] 3.3 1.7 Western Europe
## GERMANY 1.3422 [1.3418; 1.3426] 4.4 1.7 Western Europe
## GREECE 0.7983 [0.7974; 0.7991] 0.4 1.7 Southern Europe
## HUNGARY 0.2731 [0.2725; 0.2736] 0.1 1.7 Eastern Europe
## INDIA 0.0099 [0.0099; 0.0099] 0.5 1.7 Southern Asia
## IRELAND 0.9042 [0.9028; 0.9057] 0.2 1.7 Northern Europe
## ITALY 0.5370 [0.5367; 0.5373] 1.3 1.7 Southern Europe
## JAPAN 0.1478 [0.1477; 0.1479] 0.8 1.7 Eastern Asia
## JORDAN 0.0886 [0.0882; 0.0889] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0266 [0.0263; 0.0269] 0.0 1.7 Western Asia
## LATVIA 0.3719 [0.3705; 0.3732] 0.0 1.7 Northern Europe
## LEBANON 0.3655 [0.3646; 0.3664] 0.1 1.7 Western Asia
## LITHUANIA 0.2225 [0.2216; 0.2234] 0.0 1.7 Northern Europe
## LUXEMBOURG 0.7788 [0.7747; 0.7829] 0.0 1.7 Western Europe
## MEXICO 0.0599 [0.0598; 0.0600] 0.3 1.7 Central and South America and the Caribbean
## MOROCCO 0.0051 [0.0050; 0.0051] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 1.1650 [1.1633; 1.1667] 0.2 1.7 Australia and New Zealand
## NORWAY 1.4251 [1.4233; 1.4268] 0.3 1.7 Northern Europe
## PAKISTAN 0.0466 [0.0465; 0.0466] 0.3 1.7 Southern Asia
## PERU 0.0173 [0.0173; 0.0174] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0266 [0.0265; 0.0266] 0.1 1.7 South-eastern Asia
## POLAND 0.1674 [0.1672; 0.1676] 0.3 1.7 Eastern Europe
## PORTUGAL 1.1282 [1.1271; 1.1292] 0.5 1.7 Southern Europe
## PUERTO RICO 4.7038 [4.7000; 4.7075] 0.7 1.7 Central and South America and the Caribbean
## ROMANIA 0.1543 [0.1540; 0.1546] 0.1 1.7 Eastern Europe
## RUSSIA 0.0166 [0.0166; 0.0166] 0.1 1.7 Eastern Europe
## SAUDI ARABIA 0.1008 [0.1006; 0.1010] 0.1 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.7215 [0.7203; 0.7227] 0.2 1.7 Eastern Europe
## SLOVENIA 0.4132 [0.4117; 0.4146] 0.0 1.7 Southern Europe
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.7 Southern Africa
## SOUTH KOREA 0.6597 [0.6593; 0.6601] 1.3 1.7 Eastern Asia
## SPAIN 1.8188 [1.8181; 1.8194] 3.4 1.7 Southern Europe
## SWEDEN 1.2153 [1.2141; 1.2165] 0.5 1.7 Northern Europe
## SWITZERLAND 0.5953 [0.5944; 0.5962] 0.2 1.7 Western Europe
## TAIWAN 0.1779 [0.1776; 0.1781] 0.2 1.7 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0831 [0.0828; 0.0834] 0.0 1.7 Northern Africa
## TÜRKIYE 1.4377 [1.4372; 1.4381] 4.1 1.7 Western Asia
## UNITED ARAB EMIRATES 0.1115 [0.1111; 0.1119] 0.0 1.7 Western Asia
## UNITED KINGDOM 2.0046 [2.0040; 2.0052] 5.1 1.7 Northern Europe
## UNITED STATES 5.1806 [5.1802; 5.1811] 63.7 1.7 Northern America
## URUGUAY 0.2686 [0.2677; 0.2695] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.4848 [0.4844; 0.4852] 0.5 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 59
##
## rate 95%-CI z p-value
## Common effect model 2.8470 [2.8469; 2.8472] 31556.37 0
## Random effects model 0.2235 [0.1569; 0.3183] -8.30 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.9211 [1.6536; 5.4182]; tau = 1.3860 [1.2859; 2.3277]
## I^2 = 100.0%; H = 4193.46
##
## Test of heterogeneity:
## Q d.f. p-value
## 1019937617.68 58 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 4.9981 [4.9977; 4.9985] 14040867.96 100.0%
## Region = Central and South America and t ... 10 0.3685 [0.3684; 0.3687] 71856417.21 100.0%
## Region = Northern Europe 8 1.7810 [1.7805; 1.7814] 5007049.74 100.0%
## Region = Eastern Europe 8 0.2945 [0.2943; 0.2947] 14394809.46 100.0%
## Region = Southern Europe 6 1.2236 [1.2232; 1.2239] 14640133.22 100.0%
## Region = Western Europe 6 1.2828 [1.2825; 1.2831] 3594821.16 100.0%
## Region = Australia and New Zealand 2 0.8527 [0.8521; 0.8533] 232452.36 100.0%
## Region = Eastern Asia 4 0.2827 [0.2826; 0.2829] 35978409.65 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0266 [0.0265; 0.0266] 0.00 --
## Region = Southern Asia 2 0.0185 [0.0185; 0.0185] 4275621.07 100.0%
## Region = Western Asia 6 1.2660 [1.2656; 1.2664] 11764429.76 100.0%
## Region = Northern Africa 3 0.0785 [0.0785; 0.0786] 453736.78 100.0%
## Region = Southern Africa 1 0.0456 [0.0455; 0.0457] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 843698869.31 12 0
## Within groups 176238748.38 46 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.6957 [1.9064; 7.1646] 0.2282 0.4777
## Region = Central and South America and t ... 10 0.1116 [0.0310; 0.4014] 4.2651 2.0652
## Region = Northern Europe 8 0.6376 [0.4455; 0.9124] 0.2675 0.5172
## Region = Eastern Europe 8 0.1305 [0.0528; 0.3223] 1.7037 1.3053
## Region = Southern Europe 6 0.6904 [0.3904; 1.2209] 0.5076 0.7125
## Region = Western Europe 6 0.8811 [0.7034; 1.1036] 0.0792 0.2814
## Region = Australia and New Zealand 2 0.9514 [0.6397; 1.4149] 0.0820 0.2864
## Region = Eastern Asia 4 0.0765 [0.0143; 0.4097] 2.9324 1.7124
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0266 [0.0265; 0.0266] -- --
## Region = Southern Asia 2 0.0215 [0.0047; 0.0981] 1.2023 1.0965
## Region = Western Asia 6 0.1550 [0.0408; 0.5888] 2.7809 1.6676
## Region = Northern Africa 3 0.0328 [0.0129; 0.0831] 0.6774 0.8231
## Region = Southern Africa 1 0.0456 [0.0455; 0.0457] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 128255.83 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1292 [0.1290; 0.1294] 0.2 1.7 Central and South America and the Caribbean
## AUSTRALIA 0.8366 [0.8360; 0.8373] 0.6 1.7 Australia and New Zealand
## AUSTRIA 1.7240 [1.7225; 1.7255] 0.5 1.7 Western Europe
## BELARUS 0.0122 [0.0121; 0.0124] 0.0 1.7 Eastern Europe
## BELGIUM 0.4468 [0.4461; 0.4475] 0.2 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0614 [0.0614; 0.0615] 0.4 1.7 Central and South America and the Caribbean
## BULGARIA 0.1616 [0.1611; 0.1621] 0.0 1.7 Eastern Europe
## CANADA 2.7823 [2.7814; 2.7832] 3.2 1.7 Northern America
## CHILE 0.0269 [0.0268; 0.0271] 0.0 1.7 Central and South America and the Caribbean
## CHINA 0.0031 [0.0031; 0.0031] 0.1 1.7 Eastern Asia
## COLOMBIA 0.0235 [0.0234; 0.0236] 0.0 1.7 Central and South America and the Caribbean
## CROATIA 0.2668 [0.2660; 0.2676] 0.0 1.7 Southern Europe
## CZECH REPUBLIC 1.0463 [1.0452; 1.0473] 0.4 1.7 Eastern Europe
## ECUADOR 0.0720 [0.0718; 0.0723] 0.0 1.7 Central and South America and the Caribbean
## EGYPT 0.1069 [0.1067; 0.1070] 0.3 1.7 Northern Africa
## ESTONIA 0.2425 [0.2411; 0.2439] 0.0 1.7 Northern Europe
## FINLAND 0.9824 [0.9810; 0.9837] 0.2 1.7 Northern Europe
## FRANCE 1.2534 [1.2529; 1.2538] 2.7 1.7 Western Europe
## GERMANY 1.4106 [1.4101; 1.4110] 3.9 1.7 Western Europe
## GREECE 0.8032 [0.8023; 0.8041] 0.3 1.7 Southern Europe
## HUNGARY 0.3559 [0.3553; 0.3565] 0.1 1.7 Eastern Europe
## INDIA 0.0114 [0.0114; 0.0114] 0.5 1.7 Southern Asia
## IRELAND 0.8461 [0.8447; 0.8475] 0.1 1.7 Northern Europe
## ITALY 0.5452 [0.5449; 0.5455] 1.1 1.7 Southern Europe
## JAPAN 0.1810 [0.1809; 0.1811] 0.8 1.7 Eastern Asia
## JORDAN 0.1086 [0.1082; 0.1090] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0240 [0.0237; 0.0243] 0.0 1.7 Western Asia
## LATVIA 0.4715 [0.4699; 0.4730] 0.0 1.7 Northern Europe
## LEBANON 0.3637 [0.3629; 0.3646] 0.1 1.7 Western Asia
## LITHUANIA 0.2311 [0.2302; 0.2320] 0.0 1.7 Northern Europe
## LUXEMBOURG 0.7427 [0.7388; 0.7466] 0.0 1.7 Western Europe
## MEXICO 0.0597 [0.0596; 0.0597] 0.2 1.7 Central and South America and the Caribbean
## MOROCCO 0.0060 [0.0060; 0.0061] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 1.3931 [1.3913; 1.3950] 0.2 1.7 Australia and New Zealand
## NORWAY 1.8065 [1.8046; 1.8085] 0.3 1.7 Northern Europe
## PAKISTAN 0.0446 [0.0445; 0.0446] 0.3 1.7 Southern Asia
## PERU 0.0172 [0.0171; 0.0173] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0290 [0.0289; 0.0291] 0.1 1.7 South-eastern Asia
## POLAND 0.1920 [0.1917; 0.1922] 0.2 1.7 Eastern Europe
## PORTUGAL 1.1608 [1.1597; 1.1618] 0.4 1.7 Southern Europe
## PUERTO RICO 5.5328 [5.5288; 5.5368] 0.7 1.7 Central and South America and the Caribbean
## ROMANIA 0.2463 [0.2459; 0.2466] 0.2 1.7 Eastern Europe
## RUSSIA 0.0159 [0.0159; 0.0160] 0.1 1.7 Eastern Europe
## SAUDI ARABIA 0.1232 [0.1230; 0.1234] 0.1 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.8791 [0.8778; 0.8805] 0.2 1.7 Eastern Europe
## SLOVENIA 0.4079 [0.4064; 0.4093] 0.0 1.7 Southern Europe
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.7 Southern Africa
## SOUTH KOREA 0.6761 [0.6757; 0.6765] 1.1 1.7 Eastern Asia
## SPAIN 1.8213 [1.8206; 1.8219] 2.9 1.7 Southern Europe
## SWEDEN 1.2623 [1.2611; 1.2635] 0.4 1.7 Northern Europe
## SWITZERLAND 0.5748 [0.5739; 0.5757] 0.2 1.7 Western Europe
## TAIWAN 0.1970 [0.1967; 0.1973] 0.2 1.7 Eastern Asia
## THAILAND 0.1721 [0.1720; 0.1723] 0.4 1.7 South-eastern Asia
## TUNISIA 0.0815 [0.0812; 0.0818] 0.0 1.7 Northern Africa
## TÜRKIYE 1.7746 [1.7740; 1.7751] 4.3 1.7 Western Asia
## UNITED ARAB EMIRATES 0.1305 [0.1301; 0.1309] 0.0 1.7 Western Asia
## UNITED KINGDOM 2.3586 [2.3580; 2.3592] 5.1 1.7 Northern Europe
## UNITED STATES 6.3255 [6.3250; 6.3260] 66.0 1.7 Northern America
## URUGUAY 0.2401 [0.2392; 0.2409] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.4784 [0.4780; 0.4788] 0.5 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.4476 [3.4474; 3.4478] 40696.65 0
## Random effects model 0.2445 [0.1685; 0.3549] -7.41 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.1673 [1.7147; 5.6231]; tau = 1.4722 [1.3094; 2.3713]
## I^2 = 100.0%; H = 4694.40
##
## Test of heterogeneity:
## Q d.f. p-value
## 1300205778.35 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 6.0892 [6.0888; 6.0896] 22322857.16 100.0%
## Region = Central and South America and t ... 10 0.4334 [0.4332; 0.4336] 84203729.38 100.0%
## Region = Northern Europe 8 2.0853 [2.0848; 2.0858] 6851246.20 100.0%
## Region = Eastern Europe 8 0.3698 [0.3696; 0.3700] 16162172.26 100.0%
## Region = Southern Europe 6 1.2310 [1.2306; 1.2313] 14580988.44 100.0%
## Region = Western Europe 6 1.3086 [1.3083; 1.3089] 3905460.33 100.0%
## Region = Australia and New Zealand 2 0.9491 [0.9484; 0.9497] 435233.77 100.0%
## Region = Eastern Asia 4 0.2746 [0.2744; 0.2747] 42693447.47 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.1226 [0.1225; 0.1227] 2555118.07 100.0%
## Region = Southern Asia 2 0.0187 [0.0187; 0.0187] 3458367.58 100.0%
## Region = Western Asia 6 1.5620 [1.5615; 1.5624] 15136013.33 100.0%
## Region = Northern Africa 3 0.0986 [0.0985; 0.0987] 588757.35 100.0%
## Region = Southern Africa 1 0.0456 [0.0455; 0.0457] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1087312386.99 12 0
## Within groups 212893391.36 47 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 4.1952 [1.8759; 9.3820] 0.3373 0.5807
## Region = Central and South America and t ... 10 0.1090 [0.0282; 0.4213] 4.7612 2.1820
## Region = Northern Europe 8 0.7657 [0.5139; 1.1407] 0.3311 0.5754
## Region = Eastern Europe 8 0.1625 [0.0685; 0.3859] 1.5564 1.2476
## Region = Southern Europe 6 0.6821 [0.3875; 1.2008] 0.4995 0.7067
## Region = Western Europe 6 0.9136 [0.7246; 1.1519] 0.0839 0.2897
## Region = Australia and New Zealand 2 1.0796 [0.6550; 1.7795] 0.1300 0.3606
## Region = Eastern Asia 4 0.0929 [0.0172; 0.5003] 2.9520 1.7181
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.0706 [0.0123; 0.4047] 1.5861 1.2594
## Region = Southern Asia 2 0.0225 [0.0059; 0.0857] 0.9285 0.9636
## Region = Western Asia 6 0.1732 [0.0433; 0.6927] 3.0006 1.7322
## Region = Northern Africa 3 0.0375 [0.0133; 0.1054] 0.8352 0.9139
## Region = Southern Africa 1 0.0456 [0.0455; 0.0457] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1213.16 12 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1364 [0.1363; 0.1366] 0.2 1.6 Central and South America and the Caribbean
## AUSTRALIA 0.8668 [0.8662; 0.8675] 0.6 1.6 Australia and New Zealand
## AUSTRIA 1.8391 [1.8376; 1.8406] 0.5 1.6 Western Europe
## BELARUS 0.0139 [0.0138; 0.0140] 0.0 1.6 Eastern Europe
## BELGIUM 0.4884 [0.4878; 0.4891] 0.2 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.0443 [0.0439; 0.0447] 0.0 1.6 Southern Europe
## BRAZIL 0.0770 [0.0769; 0.0770] 0.5 1.6 Central and South America and the Caribbean
## BULGARIA 0.2211 [0.2206; 0.2217] 0.1 1.6 Eastern Europe
## CANADA 2.9905 [2.9896; 2.9915] 3.3 1.6 Northern America
## CHILE 0.0234 [0.0233; 0.0235] 0.0 1.6 Central and South America and the Caribbean
## CHINA 0.0047 [0.0047; 0.0047] 0.2 1.6 Eastern Asia
## COLOMBIA 0.0219 [0.0218; 0.0220] 0.0 1.6 Central and South America and the Caribbean
## CROATIA 0.2282 [0.2275; 0.2290] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.2108 [1.2097; 1.2119] 0.4 1.6 Eastern Europe
## ECUADOR 0.0672 [0.0670; 0.0675] 0.0 1.6 Central and South America and the Caribbean
## EGYPT 0.1148 [0.1147; 0.1149] 0.3 1.6 Northern Africa
## ESTONIA 0.2902 [0.2887; 0.2917] 0.0 1.6 Northern Europe
## FINLAND 1.0435 [1.0420; 1.0449] 0.2 1.6 Northern Europe
## FRANCE 1.2042 [1.2038; 1.2047] 2.4 1.6 Western Europe
## GERMANY 1.4962 [1.4957; 1.4966] 3.8 1.6 Western Europe
## GREECE 0.8916 [0.8906; 0.8925] 0.3 1.6 Southern Europe
## HUNGARY 0.4230 [0.4223; 0.4236] 0.1 1.6 Eastern Europe
## INDIA 0.0135 [0.0135; 0.0136] 0.5 1.6 Southern Asia
## IRELAND 0.8952 [0.8938; 0.8967] 0.1 1.6 Northern Europe
## ITALY 0.5448 [0.5445; 0.5451] 1.0 1.6 Southern Europe
## JAPAN 0.1505 [0.1504; 0.1506] 0.6 1.6 Eastern Asia
## JORDAN 0.1083 [0.1079; 0.1087] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0189 [0.0187; 0.0190] 0.0 1.6 Central Asia
## KUWAIT 0.0285 [0.0282; 0.0288] 0.0 1.6 Western Asia
## LATVIA 0.6091 [0.6073; 0.6108] 0.0 1.6 Northern Europe
## LEBANON 0.3338 [0.3330; 0.3346] 0.1 1.6 Western Asia
## LITHUANIA 0.2378 [0.2369; 0.2387] 0.0 1.6 Northern Europe
## LUXEMBOURG 0.7418 [0.7379; 0.7456] 0.0 1.6 Western Europe
## MEXICO 0.0580 [0.0579; 0.0581] 0.2 1.6 Central and South America and the Caribbean
## MOROCCO 0.0104 [0.0103; 0.0104] 0.0 1.6 Northern Africa
## NETHERLANDS 0.6528 [0.6522; 0.6534] 0.3 1.6 Western Europe
## NEW ZEALAND 1.6064 [1.6045; 1.6084] 0.2 1.6 Australia and New Zealand
## NORWAY 2.0397 [2.0376; 2.0418] 0.3 1.6 Northern Europe
## PAKISTAN 0.0465 [0.0465; 0.0466] 0.3 1.6 Southern Asia
## PERU 0.0192 [0.0192; 0.0193] 0.0 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0279 [0.0279; 0.0280] 0.1 1.6 South-eastern Asia
## POLAND 0.2211 [0.2208; 0.2213] 0.3 1.6 Eastern Europe
## PORTUGAL 1.1147 [1.1136; 1.1157] 0.4 1.6 Southern Europe
## PUERTO RICO 7.1769 [7.1723; 7.1815] 0.8 1.6 Central and South America and the Caribbean
## ROMANIA 0.3992 [0.3987; 0.3996] 0.3 1.6 Eastern Europe
## RUSSIA 0.0184 [0.0183; 0.0184] 0.1 1.6 Eastern Europe
## SAUDI ARABIA 0.1683 [0.1681; 0.1686] 0.2 1.6 Western Asia
## SERBIA 0.0660 [0.0657; 0.0663] 0.0 1.6 Southern Europe
## SLOVAKIA 0.9711 [0.9697; 0.9724] 0.2 1.6 Eastern Europe
## SLOVENIA 0.3702 [0.3688; 0.3715] 0.0 1.6 Southern Europe
## SOUTH AFRICA 0.0503 [0.0502; 0.0504] 0.1 1.6 Southern Africa
## SOUTH KOREA 0.6730 [0.6727; 0.6734] 1.1 1.6 Eastern Asia
## SPAIN 1.8636 [1.8630; 1.8643] 2.8 1.6 Southern Europe
## SWEDEN 1.4097 [1.4084; 1.4109] 0.4 1.6 Northern Europe
## SWITZERLAND 0.5369 [0.5360; 0.5377] 0.1 1.6 Western Europe
## TAIWAN 0.2174 [0.2171; 0.2177] 0.2 1.6 Eastern Asia
## THAILAND 0.2549 [0.2547; 0.2551] 0.5 1.6 South-eastern Asia
## TUNISIA 0.0891 [0.0888; 0.0894] 0.0 1.6 Northern Africa
## TÜRKIYE 2.1980 [2.1975; 2.1986] 5.1 1.6 Western Asia
## UNITED ARAB EMIRATES 0.1306 [0.1302; 0.1310] 0.0 1.6 Western Asia
## UNITED KINGDOM 2.7146 [2.7139; 2.7152] 5.5 1.6 Northern Europe
## UNITED STATES 6.5599 [6.5594; 6.5604] 64.5 1.6 Northern America
## URUGUAY 0.3039 [0.3029; 0.3049] 0.0 1.6 Central and South America and the Caribbean
## VENEZUELA 0.5095 [0.5091; 0.5100] 0.5 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.5448 [3.5446; 3.5450] 43059.23 0
## Random effects model 0.2490 [0.1740; 0.3565] -7.60 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.1427 [1.7383; 5.5270]; tau = 1.4638 [1.3185; 2.3510]
## I^2 = 100.0%; H = 4754.58
##
## Test of heterogeneity:
## Q d.f. p-value
## 1424179537.86 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 6.3167 [6.3162; 6.3171] 22159370.66 100.0%
## Region = Central and South America and t ... 10 0.5695 [0.5693; 0.5697] 108810655.87 100.0%
## Region = Northern Europe 8 2.3962 [2.3957; 2.3967] 8260655.14 100.0%
## Region = Eastern Europe 8 0.4438 [0.4436; 0.4440] 18059780.13 100.0%
## Region = Southern Europe 8 1.2378 [1.2375; 1.2382] 17724703.98 100.0%
## Region = Western Europe 7 1.2989 [1.2986; 1.2992] 6752609.30 100.0%
## Region = Australia and New Zealand 2 1.0217 [1.0211; 1.0224] 723732.35 100.0%
## Region = Eastern Asia 4 0.2378 [0.2378; 0.2379] 51274998.68 100.0%
## Region = Central Asia 1 0.0189 [0.0187; 0.0190] 0.00 --
## Region = South-eastern Asia 2 0.1894 [0.1893; 0.1896] 4126909.43 100.0%
## Region = Southern Asia 2 0.0205 [0.0205; 0.0205] 3157765.53 100.0%
## Region = Western Asia 6 1.9348 [1.9343; 1.9352] 19487910.69 100.0%
## Region = Northern Africa 3 0.1043 [0.1042; 0.1044] 703123.53 100.0%
## Region = Southern Africa 1 0.0503 [0.0502; 0.0504] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1162937322.57 13 0
## Within groups 261242215.29 50 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 4.4292 [2.0512; 9.5641] 0.3085 0.5554
## Region = Central and South America and t ... 10 0.1162 [0.0276; 0.4891] 5.3777 2.3190
## Region = Northern Europe 8 0.8626 [0.5704; 1.3043] 0.3561 0.5967
## Region = Eastern Europe 8 0.1991 [0.0874; 0.4539] 1.4137 1.1890
## Region = Southern Europe 8 0.3545 [0.2079; 0.6045] 0.5933 0.7703
## Region = Western Europe 7 0.8837 [0.6779; 1.1519] 0.1280 0.3578
## Region = Australia and New Zealand 2 1.1800 [0.6447; 2.1600] 0.1903 0.4362
## Region = Eastern Asia 4 0.1011 [0.0162; 0.6318] 3.4972 1.8701
## Region = Central Asia 1 0.0189 [0.0187; 0.0190] -- --
## Region = South-eastern Asia 2 0.0844 [0.0097; 0.7368] 2.4447 1.5636
## Region = Southern Asia 2 0.0251 [0.0075; 0.0842] 0.7619 0.8729
## Region = Western Asia 6 0.1919 [0.0456; 0.8063] 3.2196 1.7943
## Region = Northern Africa 3 0.0474 [0.0170; 0.1319] 0.8200 0.9055
## Region = Southern Africa 1 0.0503 [0.0502; 0.0504] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 98506.80 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1383 [0.1382; 0.1385] 0.2 1.6 Central and South America and the Caribbean
## AUSTRALIA 0.9460 [0.9453; 0.9466] 0.7 1.6 Australia and New Zealand
## AUSTRIA 1.9626 [1.9611; 1.9642] 0.5 1.6 Western Europe
## BELARUS 0.0148 [0.0147; 0.0150] 0.0 1.6 Eastern Europe
## BELGIUM 0.5723 [0.5715; 0.5730] 0.2 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.0958 [0.0953; 0.0964] 0.0 1.6 Southern Europe
## BRAZIL 0.0876 [0.0875; 0.0876] 0.5 1.6 Central and South America and the Caribbean
## BULGARIA 0.2866 [0.2859; 0.2872] 0.1 1.6 Eastern Europe
## CANADA 3.3044 [3.3034; 3.3054] 3.6 1.6 Northern America
## CHILE 0.0220 [0.0219; 0.0221] 0.0 1.6 Central and South America and the Caribbean
## CHINA 0.0083 [0.0083; 0.0083] 0.4 1.6 Eastern Asia
## COLOMBIA 0.0193 [0.0192; 0.0193] 0.0 1.6 Central and South America and the Caribbean
## CROATIA 0.2042 [0.2035; 0.2049] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.3418 [1.3407; 1.3430] 0.4 1.6 Eastern Europe
## ECUADOR 0.0590 [0.0588; 0.0592] 0.0 1.6 Central and South America and the Caribbean
## EGYPT 0.1044 [0.1043; 0.1045] 0.3 1.6 Northern Africa
## ESTONIA 0.3901 [0.3883; 0.3919] 0.0 1.6 Northern Europe
## FINLAND 1.1761 [1.1746; 1.1776] 0.2 1.6 Northern Europe
## FRANCE 1.2173 [1.2168; 1.2177] 2.4 1.6 Western Europe
## GERMANY 1.5603 [1.5599; 1.5608] 3.9 1.6 Western Europe
## GREECE 0.8239 [0.8230; 0.8248] 0.3 1.6 Southern Europe
## HUNGARY 0.4641 [0.4634; 0.4648] 0.1 1.6 Eastern Europe
## INDIA 0.0145 [0.0145; 0.0145] 0.6 1.6 Southern Asia
## IRELAND 0.9214 [0.9199; 0.9228] 0.1 1.6 Northern Europe
## ITALY 0.5310 [0.5307; 0.5313] 1.0 1.6 Southern Europe
## JAPAN 0.1244 [0.1243; 0.1245] 0.5 1.6 Eastern Asia
## JORDAN 0.0998 [0.0994; 0.1002] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0218 [0.0217; 0.0220] 0.0 1.6 Central Asia
## KUWAIT 0.0292 [0.0289; 0.0295] 0.0 1.6 Western Asia
## LATVIA 0.8136 [0.8115; 0.8156] 0.1 1.6 Northern Europe
## LEBANON 0.2992 [0.2985; 0.3000] 0.1 1.6 Western Asia
## LITHUANIA 0.2617 [0.2607; 0.2626] 0.0 1.6 Northern Europe
## LUXEMBOURG 0.7475 [0.7437; 0.7514] 0.0 1.6 Western Europe
## MEXICO 0.0640 [0.0639; 0.0641] 0.2 1.6 Central and South America and the Caribbean
## MOROCCO 0.0172 [0.0171; 0.0173] 0.0 1.6 Northern Africa
## NETHERLANDS 0.6605 [0.6599; 0.6612] 0.3 1.6 Western Europe
## NEW ZEALAND 1.8861 [1.8840; 1.8882] 0.3 1.6 Australia and New Zealand
## NORWAY 2.1749 [2.1727; 2.1770] 0.3 1.6 Northern Europe
## PAKISTAN 0.0469 [0.0469; 0.0470] 0.3 1.6 Southern Asia
## PERU 0.0190 [0.0189; 0.0191] 0.0 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0268 [0.0267; 0.0268] 0.1 1.6 South-eastern Asia
## POLAND 0.2055 [0.2053; 0.2058] 0.2 1.6 Eastern Europe
## PORTUGAL 1.1942 [1.1931; 1.1953] 0.4 1.6 Southern Europe
## PUERTO RICO 8.2103 [8.2053; 8.2152] 0.9 1.6 Central and South America and the Caribbean
## ROMANIA 0.5151 [0.5146; 0.5157] 0.3 1.6 Eastern Europe
## RUSSIA 0.0226 [0.0225; 0.0226] 0.1 1.6 Eastern Europe
## SAUDI ARABIA 0.1279 [0.1277; 0.1281] 0.1 1.6 Western Asia
## SERBIA 0.0827 [0.0823; 0.0830] 0.0 1.6 Southern Europe
## SLOVAKIA 1.0650 [1.0635; 1.0664] 0.2 1.6 Eastern Europe
## SLOVENIA 0.3544 [0.3531; 0.3558] 0.0 1.6 Southern Europe
## SOUTH AFRICA 0.0532 [0.0531; 0.0533] 0.1 1.6 Southern Africa
## SOUTH KOREA 0.7229 [0.7225; 0.7232] 1.1 1.6 Eastern Asia
## SPAIN 1.8152 [1.8146; 1.8159] 2.7 1.6 Southern Europe
## SWEDEN 1.5277 [1.5265; 1.5290] 0.5 1.6 Northern Europe
## SWITZERLAND 0.5176 [0.5168; 0.5184] 0.1 1.6 Western Europe
## TAIWAN 0.2142 [0.2139; 0.2145] 0.2 1.6 Eastern Asia
## THAILAND 0.2950 [0.2948; 0.2952] 0.6 1.6 South-eastern Asia
## TUNISIA 0.1064 [0.1061; 0.1067] 0.0 1.6 Northern Africa
## TÜRKIYE 2.3781 [2.3775; 2.3787] 5.5 1.6 Western Asia
## UNITED ARAB EMIRATES 0.0810 [0.0807; 0.0813] 0.0 1.6 Western Asia
## UNITED KINGDOM 3.2081 [3.2074; 3.2089] 6.4 1.6 Northern Europe
## UNITED STATES 6.3591 [6.3586; 6.3595] 62.0 1.6 Northern America
## URUGUAY 0.4181 [0.4169; 0.4192] 0.0 1.6 Central and South America and the Caribbean
## VENEZUELA 0.5864 [0.5860; 0.5869] 0.5 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.4442 [3.4440; 3.4444] 42403.36 0
## Random effects model 0.2674 [0.1883; 0.3797] -7.37 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.0489 [1.7405; 5.4639]; tau = 1.4314 [1.3193; 2.3375]
## I^2 = 100.0%; H = 4801.89
##
## Test of heterogeneity:
## Q d.f. p-value
## 1452664566.87 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 6.1357 [6.1352; 6.1361] 17075245.37 100.0%
## Region = Central and South America and t ... 10 0.6608 [0.6606; 0.6611] 123860258.42 100.0%
## Region = Northern Europe 8 2.8154 [2.8148; 2.8160] 10578709.84 100.0%
## Region = Eastern Europe 8 0.4962 [0.4960; 0.4964] 20800619.61 100.0%
## Region = Southern Europe 8 1.2063 [1.2060; 1.2066] 17661915.48 100.0%
## Region = Western Europe 7 1.3442 [1.3439; 1.3444] 7340926.83 100.0%
## Region = Australia and New Zealand 2 1.1476 [1.1470; 1.1483] 1055334.37 100.0%
## Region = Eastern Asia 4 0.2080 [0.2079; 0.2080] 65605634.82 100.0%
## Region = Central Asia 1 0.0218 [0.0217; 0.0220] 0.00 --
## Region = South-eastern Asia 2 0.2238 [0.2237; 0.2240] 4843774.97 100.0%
## Region = Southern Asia 2 0.0212 [0.0212; 0.0212] 2992018.07 100.0%
## Region = Western Asia 6 2.1359 [2.1353; 2.1364] 20203034.60 100.0%
## Region = Northern Africa 3 0.0950 [0.0949; 0.0951] 644510.30 100.0%
## Region = Southern Africa 1 0.0532 [0.0531; 0.0533] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1160002584.19 13 0
## Within groups 292661982.68 50 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 4.5840 [2.4134; 8.7067] 0.2143 0.4629
## Region = Central and South America and t ... 10 0.1222 [0.0287; 0.5194] 5.4529 2.3351
## Region = Northern Europe 8 0.9948 [0.6378; 1.5517] 0.4115 0.6415
## Region = Eastern Europe 8 0.2257 [0.0979; 0.5205] 1.4535 1.2056
## Region = Southern Europe 8 0.3908 [0.2287; 0.6677] 0.5977 0.7731
## Region = Western Europe 7 0.9170 [0.6991; 1.2027] 0.1341 0.3661
## Region = Australia and New Zealand 2 1.3357 [0.6793; 2.6267] 0.2381 0.4879
## Region = Eastern Asia 4 0.1125 [0.0154; 0.8223] 4.1216 2.0302
## Region = Central Asia 1 0.0218 [0.0217; 0.0220] -- --
## Region = South-eastern Asia 2 0.0889 [0.0085; 0.9334] 2.8792 1.6968
## Region = Southern Asia 2 0.0261 [0.0083; 0.0825] 0.6888 0.8300
## Region = Western Asia 6 0.1667 [0.0334; 0.8321] 4.0376 2.0094
## Region = Northern Africa 3 0.0576 [0.0242; 0.1368] 0.5843 0.7644
## Region = Southern Africa 1 0.0532 [0.0531; 0.0533] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 94778.35 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0000 0.0 0.0 Northern Africa
## ARGENTINA 0.1348 [0.1346; 0.1350] 0.2 1.6 Central and South America and the Caribbean
## AUSTRALIA 0.7847 [0.7841; 0.7853] 0.5 1.6 Australia and New Zealand
## AUSTRIA 2.0660 [2.0644; 2.0676] 0.5 1.6 Western Europe
## BELARUS 0.0221 [0.0219; 0.0222] 0.0 1.6 Eastern Europe
## BELGIUM 0.8101 [0.8093; 0.8110] 0.2 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.1360 [0.1353; 0.1366] 0.0 1.6 Southern Europe
## BRAZIL 0.0978 [0.0977; 0.0978] 0.5 1.6 Central and South America and the Caribbean
## BULGARIA 0.4003 [0.3995; 0.4010] 0.1 1.6 Eastern Europe
## CANADA 3.6682 [3.6672; 3.6693] 3.5 1.6 Northern America
## CHILE 0.0206 [0.0205; 0.0207] 0.0 1.6 Central and South America and the Caribbean
## CHINA 0.0119 [0.0119; 0.0119] 0.5 1.6 Eastern Asia
## COLOMBIA 0.0168 [0.0167; 0.0169] 0.0 1.6 Central and South America and the Caribbean
## CROATIA 0.1891 [0.1884; 0.1897] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.5003 [1.4991; 1.5015] 0.4 1.6 Eastern Europe
## ECUADOR 0.0490 [0.0488; 0.0492] 0.0 1.6 Central and South America and the Caribbean
## EGYPT 0.1103 [0.1102; 0.1104] 0.3 1.6 Northern Africa
## ESTONIA 0.5081 [0.5061; 0.5101] 0.0 1.6 Northern Europe
## FINLAND 1.4016 [1.4000; 1.4032] 0.2 1.6 Northern Europe
## FRANCE 1.2245 [1.2240; 1.2249] 2.1 1.6 Western Europe
## GERMANY 1.6432 [1.6427; 1.6436] 3.6 1.6 Western Europe
## GREECE 0.9298 [0.9289; 0.9308] 0.3 1.6 Southern Europe
## HUNGARY 0.5382 [0.5374; 0.5389] 0.1 1.6 Eastern Europe
## INDIA 0.0157 [0.0157; 0.0157] 0.5 1.6 Southern Asia
## IRELAND 0.9678 [0.9663; 0.9693] 0.1 1.6 Northern Europe
## ITALY 0.5287 [0.5284; 0.5290] 0.9 1.6 Southern Europe
## JAPAN 0.1114 [0.1113; 0.1115] 0.4 1.6 Eastern Asia
## JORDAN 0.1073 [0.1069; 0.1076] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0203 [0.0202; 0.0204] 0.0 1.6 Central Asia
## KUWAIT 0.0408 [0.0404; 0.0411] 0.0 1.6 Western Asia
## LATVIA 1.0487 [1.0464; 1.0510] 0.1 1.6 Northern Europe
## LEBANON 0.3187 [0.3180; 0.3195] 0.1 1.6 Western Asia
## LITHUANIA 0.3129 [0.3118; 0.3139] 0.0 1.6 Northern Europe
## LUXEMBOURG 0.7067 [0.7030; 0.7104] 0.0 1.6 Western Europe
## MEXICO 0.0705 [0.0704; 0.0705] 0.2 1.6 Central and South America and the Caribbean
## MOROCCO 0.0270 [0.0269; 0.0271] 0.0 1.6 Northern Africa
## NETHERLANDS 0.6591 [0.6585; 0.6598] 0.3 1.6 Western Europe
## NEW ZEALAND 2.1377 [2.1355; 2.1399] 0.3 1.6 Australia and New Zealand
## NORWAY 2.2712 [2.2690; 2.2734] 0.3 1.6 Northern Europe
## PAKISTAN 0.0450 [0.0449; 0.0450] 0.2 1.6 Southern Asia
## PERU 0.0181 [0.0180; 0.0182] 0.0 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0330 [0.0330; 0.0331] 0.1 1.6 South-eastern Asia
## POLAND 0.2660 [0.2658; 0.2663] 0.3 1.6 Eastern Europe
## PORTUGAL 1.2241 [1.2230; 1.2252] 0.3 1.6 Southern Europe
## PUERTO RICO 8.3869 [8.3819; 8.3919] 0.8 1.6 Central and South America and the Caribbean
## ROMANIA 0.6903 [0.6897; 0.6909] 0.4 1.6 Eastern Europe
## RUSSIA 0.0296 [0.0295; 0.0296] 0.1 1.6 Eastern Europe
## SAUDI ARABIA 0.1192 [0.1190; 0.1194] 0.1 1.6 Western Asia
## SERBIA 0.0894 [0.0891; 0.0897] 0.0 1.6 Southern Europe
## SLOVAKIA 1.1862 [1.1847; 1.1877] 0.2 1.6 Eastern Europe
## SLOVENIA 0.3501 [0.3488; 0.3515] 0.0 1.6 Southern Europe
## SOUTH AFRICA 0.0572 [0.0571; 0.0573] 0.1 1.6 Southern Africa
## SOUTH KOREA 0.7358 [0.7354; 0.7362] 1.0 1.6 Eastern Asia
## SPAIN 1.8345 [1.8339; 1.8352] 2.3 1.6 Southern Europe
## SWEDEN 1.8971 [1.8957; 1.8986] 0.5 1.6 Northern Europe
## SWITZERLAND 0.5102 [0.5094; 0.5110] 0.1 1.6 Western Europe
## TAIWAN 0.2000 [0.1997; 0.2003] 0.1 1.6 Eastern Asia
## THAILAND 0.4366 [0.4363; 0.4368] 0.8 1.6 South-eastern Asia
## TUNISIA 0.1107 [0.1104; 0.1111] 0.0 1.6 Northern Africa
## TÜRKIYE 2.5445 [2.5439; 2.5451] 5.3 1.6 Western Asia
## UNITED ARAB EMIRATES 0.0828 [0.0825; 0.0831] 0.0 1.6 Western Asia
## UNITED KINGDOM 3.8409 [3.8401; 3.8417] 6.8 1.6 Northern Europe
## UNITED STATES 7.3525 [7.3520; 7.3530] 63.3 1.6 Northern America
## URUGUAY 0.4117 [0.4106; 0.4128] 0.0 1.6 Central and South America and the Caribbean
## VENEZUELA 0.6841 [0.6836; 0.6846] 0.6 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.9829 [3.9827; 3.9831] 50618.55 0
## Random effects model 0.2942 [0.2051; 0.4220] -6.65 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.1685 [1.7671; 5.5888]; tau = 1.4726 [1.3293; 2.3641]
## I^2 = 100.0%; H = 5207.29
##
## Test of heterogeneity:
## Q d.f. p-value
## 1708299477.71 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 7.0878 [7.0873; 7.0883] 21658699.79 100.0%
## Region = Central and South America and t ... 10 0.6708 [0.6706; 0.6710] 126088991.84 100.0%
## Region = Northern Europe 8 3.3625 [3.3618; 3.3631] 12984168.11 100.0%
## Region = Eastern Europe 8 0.5736 [0.5734; 0.5739] 23620137.19 100.0%
## Region = Southern Europe 8 1.2226 [1.2222; 1.2229] 17899253.57 100.0%
## Region = Western Europe 7 1.4001 [1.3998; 1.4004] 7634003.02 100.0%
## Region = Australia and New Zealand 2 1.1101 [1.1094; 1.1108] 2316862.49 100.0%
## Region = Eastern Asia 4 0.1818 [0.1817; 0.1819] 72711690.90 100.0%
## Region = Central Asia 1 0.0203 [0.0202; 0.0204] 0.00 --
## Region = South-eastern Asia 2 0.3382 [0.3380; 0.3384] 7162697.36 100.0%
## Region = Southern Asia 2 0.0215 [0.0215; 0.0215] 2444520.07 100.0%
## Region = Western Asia 6 2.2917 [2.2912; 2.2922] 21934345.55 100.0%
## Region = Northern Africa 3 0.0991 [0.0990; 0.0992] 608514.47 100.0%
## Region = Southern Africa 1 0.0572 [0.0571; 0.0573] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1391235593.34 13 0
## Within groups 317063884.37 50 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 5.1933 [2.6273; 10.2654] 0.2417 0.4917
## Region = Central and South America and t ... 10 0.1210 [0.0296; 0.4939] 5.1491 2.2692
## Region = Northern Europe 8 1.1794 [0.7480; 1.8594] 0.4317 0.6570
## Region = Eastern Europe 8 0.2869 [0.1285; 0.6406] 1.3436 1.1592
## Region = Southern Europe 8 0.4155 [0.2439; 0.7077] 0.5906 0.7685
## Region = Western Europe 7 0.9687 [0.7397; 1.2686] 0.1325 0.3641
## Region = Australia and New Zealand 2 1.2952 [0.4851; 3.4582] 0.5022 0.7086
## Region = Eastern Asia 4 0.1183 [0.0157; 0.8921] 4.2519 2.0620
## Region = Central Asia 1 0.0203 [0.0202; 0.0204] -- --
## Region = South-eastern Asia 2 0.1201 [0.0096; 1.5078] 3.3337 1.8258
## Region = Southern Asia 2 0.0265 [0.0094; 0.0746] 0.5560 0.7457
## Region = Western Asia 6 0.1809 [0.0349; 0.9365] 4.2236 2.0551
## Region = Northern Africa 3 0.0691 [0.0322; 0.1480] 0.4536 0.6735
## Region = Southern Africa 1 0.0572 [0.0571; 0.0573] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 123056.79 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0031 [ 0.0031; 0.0032] 0.0 1.5 Northern Africa
## ARGENTINA 0.1286 [ 0.1284; 0.1288] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.6459 [ 0.6453; 0.6464] 0.4 1.5 Australia and New Zealand
## AUSTRIA 2.1770 [ 2.1754; 2.1786] 0.5 1.5 Western Europe
## BELARUS 0.0305 [ 0.0303; 0.0307] 0.0 1.5 Eastern Europe
## BELGIUM 1.0163 [ 1.0154; 1.0173] 0.3 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1421 [ 0.1414; 0.1427] 0.0 1.5 Southern Europe
## BRAZIL 0.1063 [ 0.1062; 0.1064] 0.5 1.5 Central and South America and the Caribbean
## BULGARIA 0.4875 [ 0.4867; 0.4884] 0.1 1.5 Eastern Europe
## CANADA 3.8718 [ 3.8707; 3.8729] 3.5 1.5 Northern America
## CHILE 0.0202 [ 0.0201; 0.0203] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0158 [ 0.0158; 0.0158] 0.6 1.5 Eastern Asia
## COLOMBIA 0.0150 [ 0.0150; 0.0151] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1837 [ 0.1830; 0.1844] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.7140 [ 1.7127; 1.7153] 0.5 1.5 Eastern Europe
## ECUADOR 0.0434 [ 0.0432; 0.0436] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1243 [ 0.1242; 0.1244] 0.3 1.5 Northern Africa
## ESTONIA 0.6371 [ 0.6349; 0.6394] 0.0 1.5 Northern Europe
## FINLAND 1.6351 [ 1.6333; 1.6368] 0.2 1.5 Northern Europe
## FRANCE 1.2639 [ 1.2634; 1.2644] 2.0 1.5 Western Europe
## GERMANY 1.6853 [ 1.6848; 1.6858] 3.4 1.5 Western Europe
## GREECE 0.9639 [ 0.9629; 0.9649] 0.3 1.5 Southern Europe
## HUNGARY 0.6103 [ 0.6095; 0.6111] 0.1 1.5 Eastern Europe
## INDIA 0.0183 [ 0.0183; 0.0183] 0.6 1.5 Southern Asia
## IRELAND 1.0037 [ 1.0022; 1.0052] 0.1 1.5 Northern Europe
## ITALY 0.5519 [ 0.5516; 0.5522] 0.8 1.5 Southern Europe
## JAPAN 0.1038 [ 0.1037; 0.1039] 0.3 1.5 Eastern Asia
## JORDAN 0.0884 [ 0.0880; 0.0887] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0272 [ 0.0270; 0.0273] 0.0 1.5 Central Asia
## KUWAIT 0.1028 [ 0.1022; 0.1033] 0.0 1.5 Western Asia
## LATVIA 1.2957 [ 1.2931; 1.2983] 0.1 1.5 Northern Europe
## LEBANON 0.3372 [ 0.3365; 0.3380] 0.1 1.5 Western Asia
## LITHUANIA 0.3355 [ 0.3344; 0.3366] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6660 [ 0.6625; 0.6696] 0.0 1.5 Western Europe
## MEXICO 0.0697 [ 0.0696; 0.0698] 0.2 1.5 Central and South America and the Caribbean
## MOROCCO 0.0311 [ 0.0310; 0.0312] 0.0 1.5 Northern Africa
## NETHERLANDS 0.6984 [ 0.6977; 0.6991] 0.3 1.5 Western Europe
## NEW ZEALAND 2.4379 [ 2.4355; 2.4403] 0.3 1.5 Australia and New Zealand
## NORWAY 2.2095 [ 2.2074; 2.2116] 0.3 1.5 Northern Europe
## PAKISTAN 0.0423 [ 0.0422; 0.0423] 0.2 1.5 Southern Asia
## PERU 0.0158 [ 0.0157; 0.0159] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0272 [ 0.0271; 0.0272] 0.1 1.5 South-eastern Asia
## POLAND 0.3332 [ 0.3329; 0.3335] 0.3 1.5 Eastern Europe
## PORTUGAL 1.2655 [ 1.2643; 1.2666] 0.3 1.5 Southern Europe
## PUERTO RICO 10.1429 [10.1373; 10.1484] 0.9 1.5 Central and South America and the Caribbean
## ROMANIA 0.8419 [ 0.8412; 0.8426] 0.4 1.5 Eastern Europe
## RUSSIA 0.0362 [ 0.0361; 0.0362] 0.1 1.5 Eastern Europe
## SAUDI ARABIA 0.1250 [ 0.1248; 0.1252] 0.1 1.5 Western Asia
## SERBIA 0.0908 [ 0.0905; 0.0912] 0.0 1.5 Southern Europe
## SLOVAKIA 1.4205 [ 1.4189; 1.4222] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3401 [ 0.3388; 0.3414] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0581 [ 0.0580; 0.0582] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.8166 [ 0.8162; 0.8170] 1.0 1.5 Eastern Asia
## SPAIN 1.8863 [ 1.8856; 1.8869] 2.2 1.5 Southern Europe
## SWEDEN 2.3524 [ 2.3508; 2.3540] 0.6 1.5 Northern Europe
## SWITZERLAND 0.5117 [ 0.5109; 0.5125] 0.1 1.5 Western Europe
## TAIWAN 0.1917 [ 0.1914; 0.1920] 0.1 1.5 Eastern Asia
## THAILAND 0.4858 [ 0.4856; 0.4861] 0.8 1.5 South-eastern Asia
## TUNISIA 0.1231 [ 0.1227; 0.1234] 0.0 1.5 Northern Africa
## TÜRKIYE 2.5723 [ 2.5717; 2.5729] 5.0 1.5 Western Asia
## UNITED ARAB EMIRATES 0.0897 [ 0.0893; 0.0900] 0.0 1.5 Western Asia
## UNITED KINGDOM 4.4910 [ 4.4902; 4.4919] 7.4 1.5 Northern Europe
## UNITED STATES 7.9211 [ 7.9206; 7.9216] 63.3 1.5 Northern America
## URUGUAY 0.4417 [ 0.4405; 0.4429] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.8366 [ 0.8360; 0.8371] 0.6 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.2944 [4.2941; 4.2946] 55636.38 0
## Random effects model 0.2970 [0.2069; 0.4264] -6.58 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.2140 [1.8122; 5.7187]; tau = 1.4879 [1.3462; 2.3914]
## I^2 = 100.0%; H = 5444.97
##
## Test of heterogeneity:
## Q d.f. p-value
## 1897453214.78 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 7.6324 [7.6319; 7.6329] 24502017.82 100.0%
## Region = Central and South America and t ... 10 0.8601 [0.8598; 0.8604] 150242826.57 100.0%
## Region = Northern Europe 8 3.9290 [3.9283; 3.9297] 15545676.98 100.0%
## Region = Eastern Europe 8 0.6699 [0.6696; 0.6701] 27464690.33 100.0%
## Region = Southern Europe 8 1.2578 [1.2575; 1.2582] 18217237.30 100.0%
## Region = Western Europe 7 1.4488 [1.4485; 1.4491] 7395333.57 100.0%
## Region = Australia and New Zealand 2 1.1316 [1.1309; 1.1323] 4146297.98 100.0%
## Region = Eastern Asia 4 0.1838 [0.1837; 0.1838] 83779536.81 100.0%
## Region = Central Asia 1 0.0272 [0.0270; 0.0273] 0.00 --
## Region = South-eastern Asia 2 0.3903 [0.3901; 0.3905] 7666743.77 100.0%
## Region = Southern Asia 2 0.0227 [0.0227; 0.0227] 1570697.60 100.0%
## Region = Western Asia 6 2.3055 [2.3050; 2.3060] 23294204.76 100.0%
## Region = Northern Africa 4 0.1080 [0.1079; 0.1081] 1250157.76 100.0%
## Region = Southern Africa 1 0.0581 [0.0580; 0.0582] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1532377793.55 13 0
## Within groups 365075421.23 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 5.5380 [2.7460; 11.1686] 0.2562 0.5062
## Region = Central and South America and t ... 10 0.1222 [0.0286; 0.5224] 5.4927 2.3437
## Region = Northern Europe 8 1.3434 [0.8434; 2.1398] 0.4513 0.6718
## Region = Eastern Europe 8 0.3499 [0.1584; 0.7725] 1.3067 1.1431
## Region = Southern Europe 8 0.4229 [0.2493; 0.7174] 0.5814 0.7625
## Region = Western Europe 7 1.0166 [0.7852; 1.3162] 0.1216 0.3487
## Region = Australia and New Zealand 2 1.2548 [0.3414; 4.6121] 0.8822 0.9392
## Region = Eastern Asia 4 0.1265 [0.0161; 0.9959] 4.4318 2.1052
## Region = Central Asia 1 0.0272 [0.0270; 0.0273] -- --
## Region = South-eastern Asia 2 0.1149 [0.0068; 1.9386] 4.1561 2.0387
## Region = Southern Asia 2 0.0278 [0.0122; 0.0632] 0.3511 0.5925
## Region = Western Asia 6 0.2110 [0.0417; 1.0679] 4.1071 2.0266
## Region = Northern Africa 4 0.0350 [0.0147; 0.0829] 0.7766 0.8813
## Region = Southern Africa 1 0.0581 [0.0580; 0.0582] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 87488.21 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0272 [ 0.0271; 0.0273] 0.0 1.5 Northern Africa
## ARGENTINA 0.1302 [ 0.1300; 0.1303] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.6512 [ 0.6507; 0.6518] 0.3 1.5 Australia and New Zealand
## AUSTRIA 2.1760 [ 2.1744; 2.1777] 0.4 1.5 Western Europe
## BELARUS 0.0476 [ 0.0474; 0.0478] 0.0 1.5 Eastern Europe
## BELGIUM 1.0938 [ 1.0927; 1.0948] 0.3 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1982 [ 0.1974; 0.1990] 0.0 1.5 Southern Europe
## BRAZIL 0.1118 [ 0.1118; 0.1119] 0.5 1.5 Central and South America and the Caribbean
## BULGARIA 0.5419 [ 0.5410; 0.5428] 0.1 1.5 Eastern Europe
## CANADA 4.1539 [ 4.1528; 4.1550] 3.3 1.5 Northern America
## CHILE 0.0193 [ 0.0192; 0.0194] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0202 [ 0.0202; 0.0202] 0.6 1.5 Eastern Asia
## COLOMBIA 0.0150 [ 0.0149; 0.0150] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1533 [ 0.1527; 0.1539] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.8848 [ 1.8834; 1.8861] 0.4 1.5 Eastern Europe
## ECUADOR 0.0448 [ 0.0447; 0.0450] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1340 [ 0.1339; 0.1342] 0.3 1.5 Northern Africa
## ESTONIA 0.7565 [ 0.7540; 0.7589] 0.0 1.5 Northern Europe
## FINLAND 1.8835 [ 1.8816; 1.8854] 0.2 1.5 Northern Europe
## FRANCE 1.2950 [ 1.2946; 1.2955] 1.8 1.5 Western Europe
## GERMANY 1.6790 [ 1.6785; 1.6794] 3.0 1.5 Western Europe
## GREECE 1.0152 [ 1.0142; 1.0162] 0.2 1.5 Southern Europe
## HUNGARY 0.6659 [ 0.6651; 0.6668] 0.1 1.5 Eastern Europe
## INDIA 0.0197 [ 0.0197; 0.0197] 0.6 1.5 Southern Asia
## IRELAND 1.0668 [ 1.0652; 1.0684] 0.1 1.5 Northern Europe
## ITALY 0.5614 [ 0.5610; 0.5617] 0.8 1.5 Southern Europe
## JAPAN 0.0968 [ 0.0967; 0.0969] 0.3 1.5 Eastern Asia
## JORDAN 0.0728 [ 0.0725; 0.0731] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0261 [ 0.0259; 0.0262] 0.0 1.5 Central Asia
## KUWAIT 0.1504 [ 0.1497; 0.1510] 0.0 1.5 Western Asia
## LATVIA 1.5928 [ 1.5899; 1.5957] 0.1 1.5 Northern Europe
## LEBANON 0.3553 [ 0.3545; 0.3560] 0.1 1.5 Western Asia
## LITHUANIA 0.3776 [ 0.3765; 0.3788] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6946 [ 0.6910; 0.6982] 0.0 1.5 Western Europe
## MEXICO 0.0747 [ 0.0746; 0.0748] 0.2 1.5 Central and South America and the Caribbean
## MOROCCO 0.0421 [ 0.0420; 0.0423] 0.0 1.5 Northern Africa
## NETHERLANDS 0.7221 [ 0.7215; 0.7228] 0.3 1.5 Western Europe
## NEW ZEALAND 2.8493 [ 2.8467; 2.8518] 0.3 1.5 Australia and New Zealand
## NORWAY 2.6628 [ 2.6605; 2.6651] 0.3 1.5 Northern Europe
## PAKISTAN 0.0398 [ 0.0397; 0.0398] 0.2 1.5 Southern Asia
## PERU 0.0558 [ 0.0557; 0.0560] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0275 [ 0.0274; 0.0275] 0.1 1.5 South-eastern Asia
## POLAND 0.4023 [ 0.4020; 0.4027] 0.3 1.5 Eastern Europe
## PORTUGAL 1.3129 [ 1.3117; 1.3140] 0.3 1.5 Southern Europe
## PUERTO RICO 11.7963 [11.7902; 11.8024] 0.9 1.5 Central and South America and the Caribbean
## ROMANIA 0.9553 [ 0.9545; 0.9560] 0.4 1.5 Eastern Europe
## RUSSIA 0.0453 [ 0.0453; 0.0454] 0.1 1.5 Eastern Europe
## SAUDI ARABIA 0.2044 [ 0.2042; 0.2047] 0.1 1.5 Western Asia
## SERBIA 0.1135 [ 0.1131; 0.1138] 0.0 1.5 Southern Europe
## SLOVAKIA 1.5663 [ 1.5645; 1.5680] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3335 [ 0.3322; 0.3348] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0551 [ 0.0550; 0.0552] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.8380 [ 0.8375; 0.8384] 0.9 1.5 Eastern Asia
## SPAIN 1.9664 [ 1.9657; 1.9670] 2.0 1.5 Southern Europe
## SWEDEN 2.7401 [ 2.7384; 2.7418] 0.6 1.5 Northern Europe
## SWITZERLAND 0.5081 [ 0.5073; 0.5089] 0.1 1.5 Western Europe
## TAIWAN 0.2013 [ 0.2010; 0.2016] 0.1 1.5 Eastern Asia
## THAILAND 0.5087 [ 0.5084; 0.5090] 0.8 1.5 South-eastern Asia
## TUNISIA 0.0958 [ 0.0955; 0.0961] 0.0 1.5 Northern Africa
## TÜRKIYE 2.6150 [ 2.6144; 2.6156] 4.5 1.5 Western Asia
## UNITED ARAB EMIRATES 0.1007 [ 0.1004; 0.1011] 0.0 1.5 Western Asia
## UNITED KINGDOM 5.1176 [ 5.1167; 5.1185] 7.5 1.5 Northern Europe
## UNITED STATES 9.2026 [ 9.2020; 9.2031] 65.3 1.5 Northern America
## URUGUAY 0.3638 [ 0.3627; 0.3649] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.3521 [ 0.3517; 0.3524] 0.2 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.0243 [5.0241; 5.0246] 65592.18 0
## Random effects model 0.3330 [0.2283; 0.4855] -5.71 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.4074 [1.8765; 5.9757]; tau = 1.5516 [1.3699; 2.4445]
## I^2 = 100.0%; H = 5908.92
##
## Test of heterogeneity:
## Q d.f. p-value
## 2234581732.27 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 8.8562 [8.8557; 8.8567] 32916135.44 100.0%
## Region = Central and South America and t ... 10 0.8926 [0.8923; 0.8929] 178240640.75 100.0%
## Region = Northern Europe 8 4.4871 [4.4863; 4.4878] 17341214.90 100.0%
## Region = Eastern Europe 8 0.7319 [0.7316; 0.7321] 30772569.15 100.0%
## Region = Southern Europe 8 1.3070 [1.3066; 1.3073] 19356305.49 100.0%
## Region = Western Europe 7 1.4589 [1.4586; 1.4592] 6901479.64 100.0%
## Region = Australia and New Zealand 2 1.2795 [1.2787; 1.2803] 5674568.71 100.0%
## Region = Eastern Asia 4 0.1724 [0.1724; 0.1725] 88154541.10 100.0%
## Region = Central Asia 1 0.0261 [0.0259; 0.0262] 0.00 --
## Region = South-eastern Asia 2 0.4096 [0.4094; 0.4098] 8079936.31 100.0%
## Region = Southern Asia 2 0.0232 [0.0232; 0.0232] 1099630.39 100.0%
## Region = Western Asia 6 2.2947 [2.2942; 2.2952] 25907039.84 100.0%
## Region = Northern Africa 4 0.1059 [0.1058; 0.1060] 1437448.83 100.0%
## Region = Southern Africa 1 0.0551 [0.0550; 0.0552] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1818700221.72 13 0
## Within groups 415881510.56 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 6.1828 [2.8356; 13.4810] 0.3164 0.5625
## Region = Central and South America and t ... 10 0.1281 [0.0242; 0.6780] 7.2292 2.6887
## Region = Northern Europe 8 1.5547 [0.9868; 2.4496] 0.4304 0.6560
## Region = Eastern Europe 8 0.4152 [0.1894; 0.9100] 1.2824 1.1324
## Region = Southern Europe 8 0.4503 [0.2636; 0.7693] 0.5972 0.7728
## Region = Western Europe 7 1.0403 [0.8128; 1.3315] 0.1110 0.3331
## Region = Australia and New Zealand 2 1.3622 [0.3207; 5.7863] 1.0892 1.0437
## Region = Eastern Asia 4 0.1347 [0.0177; 1.0234] 4.2811 2.0691
## Region = Central Asia 1 0.0261 [0.0259; 0.0262] -- --
## Region = South-eastern Asia 2 0.1182 [0.0068; 2.0651] 4.2601 2.0640
## Region = Southern Asia 2 0.0280 [0.0140; 0.0558] 0.2481 0.4981
## Region = Western Asia 6 0.2437 [0.0564; 1.0533] 3.3466 1.8294
## Region = Northern Africa 4 0.0619 [0.0282; 0.1358] 0.6420 0.8012
## Region = Southern Africa 1 0.0551 [0.0550; 0.0552] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 82625.28 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0677 [ 0.0676; 0.0678] 0.1 1.5 Northern Africa
## ARGENTINA 0.1252 [ 0.1251; 0.1254] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.6366 [ 0.6361; 0.6371] 0.3 1.5 Australia and New Zealand
## AUSTRIA 2.1089 [ 2.1074; 2.1105] 0.4 1.5 Western Europe
## BELARUS 0.0535 [ 0.0532; 0.0537] 0.0 1.5 Eastern Europe
## BELGIUM 0.9712 [ 0.9703; 0.9722] 0.2 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.2626 [ 0.2617; 0.2635] 0.0 1.5 Southern Europe
## BRAZIL 0.1122 [ 0.1121; 0.1123] 0.5 1.5 Central and South America and the Caribbean
## BULGARIA 0.5557 [ 0.5548; 0.5566] 0.1 1.5 Eastern Europe
## CANADA 4.5476 [ 4.5464; 4.5487] 3.3 1.5 Northern America
## CHILE 0.0195 [ 0.0194; 0.0196] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0238 [ 0.0238; 0.0238] 0.7 1.5 Eastern Asia
## COLOMBIA 0.0147 [ 0.0147; 0.0148] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1439 [ 0.1433; 0.1445] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.9731 [ 1.9717; 1.9745] 0.4 1.5 Eastern Europe
## ECUADOR 0.0484 [ 0.0483; 0.0486] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1568 [ 0.1567; 0.1570] 0.3 1.5 Northern Africa
## ESTONIA 0.8935 [ 0.8908; 0.8961] 0.0 1.5 Northern Europe
## FINLAND 2.2200 [ 2.2180; 2.2221] 0.2 1.5 Northern Europe
## FRANCE 1.3220 [ 1.3215; 1.3224] 1.7 1.5 Western Europe
## GERMANY 1.6877 [ 1.6872; 1.6881] 2.8 1.5 Western Europe
## GREECE 1.0272 [ 1.0262; 1.0283] 0.2 1.5 Southern Europe
## HUNGARY 0.7149 [ 0.7140; 0.7157] 0.1 1.5 Eastern Europe
## INDIA 0.0211 [ 0.0211; 0.0212] 0.6 1.5 Southern Asia
## IRELAND 1.1315 [ 1.1300; 1.1331] 0.1 1.5 Northern Europe
## ITALY 0.5573 [ 0.5569; 0.5576] 0.7 1.5 Southern Europe
## JAPAN 0.0910 [ 0.0909; 0.0910] 0.2 1.5 Eastern Asia
## JORDAN 0.0856 [ 0.0852; 0.0859] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0254 [ 0.0253; 0.0255] 0.0 1.5 Central Asia
## KUWAIT 0.4524 [ 0.4513; 0.4535] 0.0 1.5 Western Asia
## LATVIA 1.7258 [ 1.7228; 1.7288] 0.1 1.5 Northern Europe
## LEBANON 0.4366 [ 0.4357; 0.4374] 0.1 1.5 Western Asia
## LITHUANIA 0.4294 [ 0.4282; 0.4307] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6676 [ 0.6641; 0.6710] 0.0 1.5 Western Europe
## MEXICO 0.0878 [ 0.0877; 0.0879] 0.2 1.5 Central and South America and the Caribbean
## MOROCCO 0.0514 [ 0.0512; 0.0515] 0.0 1.5 Northern Africa
## NETHERLANDS 0.7640 [ 0.7633; 0.7647] 0.3 1.5 Western Europe
## NEW ZEALAND 3.3486 [ 3.3459; 3.3514] 0.3 1.5 Australia and New Zealand
## NORWAY 2.7479 [ 2.7456; 2.7503] 0.3 1.5 Northern Europe
## PAKISTAN 0.0364 [ 0.0364; 0.0365] 0.1 1.5 Southern Asia
## PERU 0.0566 [ 0.0565; 0.0568] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0275 [ 0.0274; 0.0275] 0.1 1.5 South-eastern Asia
## POLAND 0.4448 [ 0.4445; 0.4452] 0.3 1.5 Eastern Europe
## PORTUGAL 1.3466 [ 1.3455; 1.3478] 0.3 1.5 Southern Europe
## PUERTO RICO 13.2279 [13.2214; 13.2344] 0.9 1.5 Central and South America and the Caribbean
## ROMANIA 1.1118 [ 1.1110; 1.1125] 0.4 1.5 Eastern Europe
## RUSSIA 0.0930 [ 0.0930; 0.0931] 0.3 1.5 Eastern Europe
## SAUDI ARABIA 0.4083 [ 0.4079; 0.4087] 0.3 1.5 Western Asia
## SERBIA 0.1056 [ 0.1052; 0.1060] 0.0 1.5 Southern Europe
## SLOVAKIA 1.4980 [ 1.4963; 1.4997] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3248 [ 0.3235; 0.3261] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0642 [ 0.0640; 0.0643] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.8851 [ 0.8847; 0.8855] 0.9 1.5 Eastern Asia
## SPAIN 2.0645 [ 2.0638; 2.0652] 1.9 1.5 Southern Europe
## SWEDEN 3.4050 [ 3.4031; 3.4069] 0.7 1.5 Northern Europe
## SWITZERLAND 0.5020 [ 0.5012; 0.5028] 0.1 1.5 Western Europe
## TAIWAN 0.2339 [ 0.2335; 0.2342] 0.1 1.5 Eastern Asia
## THAILAND 0.5326 [ 0.5324; 0.5329] 0.7 1.5 South-eastern Asia
## TUNISIA 0.0939 [ 0.0936; 0.0942] 0.0 1.5 Northern Africa
## TÜRKIYE 2.4956 [ 2.4950; 2.4961] 4.0 1.5 Western Asia
## UNITED ARAB EMIRATES 0.0726 [ 0.0723; 0.0729] 0.0 1.5 Western Asia
## UNITED KINGDOM 5.7608 [ 5.7598; 5.7617] 7.6 1.5 Northern Europe
## UNITED STATES 10.2970 [10.2964; 10.2975] 66.5 1.5 Northern America
## URUGUAY 0.3608 [ 0.3598; 0.3619] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.0491 [ 0.0490; 0.0493] 0.0 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.6449 [5.6446; 5.6451] 73972.31 0
## Random effects model 0.3564 [0.2419; 0.5250] -5.22 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.5391 [1.9112; 6.1269]; tau = 1.5934 [1.3825; 2.4753]
## I^2 = 100.0%; H = 6297.32
##
## Test of heterogeneity:
## Q d.f. p-value
## 2537997217.08 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 9.9058 [9.9053; 9.9064] 38450605.61 100.0%
## Region = Central and South America and t ... 10 1.0740 [1.0736; 1.0744] 197938956.71 100.0%
## Region = Northern Europe 8 5.0676 [5.0669; 5.0684] 19099065.99 100.0%
## Region = Eastern Europe 8 0.7082 [0.7080; 0.7085] 34331254.58 100.0%
## Region = Southern Europe 8 1.3656 [1.3652; 1.3660] 20997743.55 100.0%
## Region = Western Europe 7 1.4638 [1.4635; 1.4641] 6776855.64 100.0%
## Region = Australia and New Zealand 2 1.4662 [1.4653; 1.4670] 7813323.91 100.0%
## Region = Eastern Asia 4 0.1750 [0.1749; 0.1750] 94218049.76 100.0%
## Region = Central Asia 1 0.0254 [0.0253; 0.0255] 0.00 --
## Region = South-eastern Asia 2 0.4303 [0.4301; 0.4305] 8485068.19 100.0%
## Region = Southern Asia 2 0.0237 [0.0237; 0.0237] 634637.49 100.0%
## Region = Western Asia 6 2.1041 [2.1037; 2.1046] 25194165.00 100.0%
## Region = Northern Africa 4 0.1236 [0.1235; 0.1237] 1207406.76 100.0%
## Region = Southern Africa 1 0.0642 [0.0640; 0.0643] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2082850083.88 13 0
## Within groups 455147133.20 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 6.8430 [3.0720; 15.2430] 0.3340 0.5779
## Region = Central and South America and t ... 10 0.1086 [0.0169; 0.6966] 8.9927 2.9988
## Region = Northern Europe 8 1.7542 [1.1264; 2.7318] 0.4086 0.6392
## Region = Eastern Europe 8 0.4815 [0.2235; 1.0374] 1.2270 1.1077
## Region = Southern Europe 8 0.4617 [0.2654; 0.8030] 0.6380 0.7988
## Region = Western Europe 7 1.0227 [0.8012; 1.3054] 0.1085 0.3294
## Region = Australia and New Zealand 2 1.4601 [0.2870; 7.4289] 1.3780 1.1739
## Region = Eastern Asia 4 0.1455 [0.0195; 1.0886] 4.2167 2.0535
## Region = Central Asia 1 0.0254 [0.0253; 0.0255] -- --
## Region = South-eastern Asia 2 0.1210 [0.0066; 2.2101] 4.3942 2.0962
## Region = Southern Asia 2 0.0278 [0.0163; 0.0473] 0.1481 0.3848
## Region = Western Asia 6 0.3282 [0.1090; 0.9884] 1.8984 1.3778
## Region = Northern Africa 4 0.0846 [0.0469; 0.1527] 0.3634 0.6028
## Region = Southern Africa 1 0.0642 [0.0640; 0.0643] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 127148.10 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.1275 [ 0.1273; 0.1277] 0.1 1.5 Northern Africa
## ARGENTINA 0.1241 [ 0.1240; 0.1243] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.6583 [ 0.6578; 0.6589] 0.3 1.5 Australia and New Zealand
## AUSTRIA 2.0880 [ 2.0864; 2.0896] 0.3 1.5 Western Europe
## BELARUS 0.0843 [ 0.0840; 0.0846] 0.0 1.5 Eastern Europe
## BELGIUM 0.9062 [ 0.9053; 0.9071] 0.2 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.3009 [ 0.3000; 0.3019] 0.0 1.5 Southern Europe
## BRAZIL 0.1148 [ 0.1148; 0.1149] 0.4 1.5 Central and South America and the Caribbean
## BULGARIA 0.5224 [ 0.5215; 0.5233] 0.1 1.5 Eastern Europe
## CANADA 4.7693 [ 4.7681; 4.7705] 3.2 1.5 Northern America
## CHILE 0.0198 [ 0.0197; 0.0200] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0293 [ 0.0293; 0.0293] 0.8 1.5 Eastern Asia
## COLOMBIA 0.0119 [ 0.0118; 0.0119] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1282 [ 0.1276; 0.1288] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.0591 [ 2.0577; 2.0606] 0.4 1.5 Eastern Europe
## ECUADOR 0.0520 [ 0.0519; 0.0522] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1489 [ 0.1488; 0.1491] 0.3 1.5 Northern Africa
## ESTONIA 1.1053 [ 1.1024; 1.1083] 0.0 1.5 Northern Europe
## FINLAND 2.6654 [ 2.6631; 2.6676] 0.3 1.5 Northern Europe
## FRANCE 1.3419 [ 1.3415; 1.3424] 1.6 1.5 Western Europe
## GERMANY 1.6769 [ 1.6765; 1.6774] 2.5 1.5 Western Europe
## GREECE 1.0026 [ 1.0016; 1.0036] 0.2 1.5 Southern Europe
## HUNGARY 0.7477 [ 0.7468; 0.7486] 0.1 1.5 Eastern Europe
## INDIA 0.0231 [ 0.0231; 0.0232] 0.6 1.5 Southern Asia
## IRELAND 1.1919 [ 1.1903; 1.1936] 0.1 1.5 Northern Europe
## ITALY 0.5694 [ 0.5691; 0.5698] 0.6 1.5 Southern Europe
## JAPAN 0.0856 [ 0.0855; 0.0857] 0.2 1.5 Eastern Asia
## JORDAN 0.1192 [ 0.1189; 0.1196] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0230 [ 0.0229; 0.0231] 0.0 1.5 Central Asia
## KUWAIT 0.9078 [ 0.9062; 0.9093] 0.1 1.5 Western Asia
## LATVIA 2.0008 [ 1.9975; 2.0041] 0.1 1.5 Northern Europe
## LEBANON 0.4970 [ 0.4962; 0.4979] 0.1 1.5 Western Asia
## LITHUANIA 0.5211 [ 0.5197; 0.5225] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6295 [ 0.6261; 0.6328] 0.0 1.5 Western Europe
## MEXICO 0.0978 [ 0.0977; 0.0979] 0.2 1.5 Central and South America and the Caribbean
## MOROCCO 0.0369 [ 0.0368; 0.0370] 0.0 1.5 Northern Africa
## NETHERLANDS 0.7887 [ 0.7880; 0.7894] 0.2 1.5 Western Europe
## NEW ZEALAND 3.7529 [ 3.7500; 3.7558] 0.3 1.5 Australia and New Zealand
## NORWAY 2.9508 [ 2.9484; 2.9532] 0.3 1.5 Northern Europe
## PAKISTAN 0.0311 [ 0.0310; 0.0311] 0.1 1.5 Southern Asia
## PERU 0.0733 [ 0.0732; 0.0735] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0243 [ 0.0242; 0.0243] 0.0 1.5 South-eastern Asia
## POLAND 0.4682 [ 0.4678; 0.4685] 0.3 1.5 Eastern Europe
## PORTUGAL 1.4084 [ 1.4072; 1.4096] 0.3 1.5 Southern Europe
## PUERTO RICO 14.8281 [14.8211; 14.8351] 0.9 1.5 Central and South America and the Caribbean
## ROMANIA 1.3080 [ 1.3072; 1.3089] 0.5 1.5 Eastern Europe
## RUSSIA 0.1532 [ 0.1531; 0.1533] 0.4 1.5 Eastern Europe
## SAUDI ARABIA 0.5669 [ 0.5665; 0.5673] 0.3 1.5 Western Asia
## SERBIA 0.1059 [ 0.1055; 0.1062] 0.0 1.5 Southern Europe
## SLOVAKIA 1.5627 [ 1.5609; 1.5644] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3402 [ 0.3389; 0.3415] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0655 [ 0.0654; 0.0656] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.9343 [ 0.9339; 0.9348] 0.9 1.5 Eastern Asia
## SPAIN 2.1655 [ 2.1648; 2.1662] 1.8 1.5 Southern Europe
## SWEDEN 3.9049 [ 3.9029; 3.9070] 0.7 1.5 Northern Europe
## SWITZERLAND 0.5067 [ 0.5059; 0.5075] 0.1 1.5 Western Europe
## TAIWAN 0.2404 [ 0.2401; 0.2407] 0.1 1.5 Eastern Asia
## THAILAND 0.6236 [ 0.6233; 0.6239] 0.8 1.5 South-eastern Asia
## TUNISIA 0.0915 [ 0.0912; 0.0918] 0.0 1.5 Northern Africa
## TÜRKIYE 2.5773 [ 2.5767; 2.5779] 3.8 1.5 Western Asia
## UNITED ARAB EMIRATES 0.0593 [ 0.0590; 0.0595] 0.0 1.5 Western Asia
## UNITED KINGDOM 6.1083 [ 6.1073; 6.1093] 7.4 1.5 Northern Europe
## UNITED STATES 11.4514 [11.4508; 11.4520] 67.5 1.5 Northern America
## URUGUAY 0.3376 [ 0.3365; 0.3386] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.0634 [ 0.0633; 0.0636] 0.0 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.2513 [6.2510; 6.2515] 82240.06 0
## Random effects model 0.3841 [0.2583; 0.5714] -4.72 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.6674 [1.9187; 6.1656]; tau = 1.6332 [1.3852; 2.4831]
## I^2 = 100.0%; H = 6695.71
##
## Test of heterogeneity:
## Q d.f. p-value
## 2869284786.03 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 11.0094 [11.0088; 11.0099] 46885844.31 100.0%
## Region = Central and South America and t ... 10 1.1832 [ 1.1828; 1.1836] 217633247.51 100.0%
## Region = Northern Europe 8 5.3963 [ 5.3955; 5.3971] 18935583.40 100.0%
## Region = Eastern Europe 8 0.7213 [ 0.7211; 0.7215] 36237051.22 100.0%
## Region = Southern Europe 8 1.4283 [ 1.4280; 1.4287] 22421508.04 100.0%
## Region = Western Europe 7 1.4622 [ 1.4619; 1.4625] 6626712.57 100.0%
## Region = Australia and New Zealand 2 1.6320 [ 1.6311; 1.6329] 9341458.86 100.0%
## Region = Eastern Asia 4 0.1743 [ 0.1742; 0.1744] 99749180.39 100.0%
## Region = Central Asia 1 0.0230 [ 0.0229; 0.0231] 0.00 --
## Region = South-eastern Asia 2 0.5202 [ 0.5200; 0.5205] 9276525.80 100.0%
## Region = Southern Asia 2 0.0243 [ 0.0243; 0.0244] 168760.36 100.0%
## Region = Western Asia 6 2.1441 [ 2.1437; 2.1446] 24556919.29 100.0%
## Region = Northern Africa 4 0.1290 [ 0.1289; 0.1291] 904429.41 100.0%
## Region = Southern Africa 1 0.0655 [ 0.0654; 0.0656] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2376547564.86 13 0
## Within groups 492737221.16 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 7.3902 [3.1323; 17.4360] 0.3836 0.6194
## Region = Central and South America and t ... 10 0.1148 [0.0175; 0.7539] 9.2215 3.0367
## Region = Northern Europe 8 2.0017 [1.3233; 3.0279] 0.3567 0.5973
## Region = Eastern Europe 8 0.5619 [0.2699; 1.1698] 1.1200 1.0583
## Region = Southern Europe 8 0.4709 [0.2676; 0.8287] 0.6652 0.8156
## Region = Western Europe 7 1.0099 [0.7938; 1.2847] 0.1056 0.3250
## Region = Australia and New Zealand 2 1.5718 [0.2855; 8.6535] 1.5148 1.2308
## Region = Eastern Asia 4 0.1541 [0.0212; 1.1194] 4.0955 2.0237
## Region = Central Asia 1 0.0230 [0.0229; 0.0231] -- --
## Region = South-eastern Asia 2 0.1230 [0.0051; 2.9621] 5.2691 2.2954
## Region = Southern Asia 2 0.0268 [0.0201; 0.0358] 0.0432 0.2079
## Region = Western Asia 6 0.4087 [0.1621; 1.0305] 1.3362 1.1559
## Region = Northern Africa 4 0.0895 [0.0565; 0.1418] 0.2205 0.4696
## Region = Southern Africa 1 0.0655 [0.0654; 0.0656] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 150775.29 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 1
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0899 [ 0.0898; 0.0901] 0.1 1.5 Northern Africa
## ARGENTINA 0.1258 [ 0.1257; 0.1260] 0.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 0.7003 [ 0.6998; 0.7009] 0.3 1.5 Australia and New Zealand
## AUSTRIA 2.0631 [ 2.0615; 2.0646] 0.3 1.5 Western Europe
## BELARUS 0.1204 [ 0.1200; 0.1208] 0.0 1.5 Eastern Europe
## BELGIUM 0.8770 [ 0.8761; 0.8779] 0.2 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.3650 [ 0.3640; 0.3661] 0.0 1.5 Southern Europe
## BRAZIL 0.1201 [ 0.1201; 0.1202] 0.4 1.5 Central and South America and the Caribbean
## BULGARIA 0.4317 [ 0.4309; 0.4325] 0.1 1.5 Eastern Europe
## CANADA 4.8245 [ 4.8233; 4.8256] 3.2 1.5 Northern America
## CHILE 0.0214 [ 0.0213; 0.0215] 0.0 1.5 Central and South America and the Caribbean
## CHINA 0.0330 [ 0.0329; 0.0330] 0.8 1.5 Eastern Asia
## COLOMBIA 0.0146 [ 0.0145; 0.0146] 0.0 1.5 Central and South America and the Caribbean
## CROATIA 0.1217 [ 0.1211; 0.1223] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.0989 [ 2.0974; 2.1003] 0.4 1.5 Eastern Europe
## ECUADOR 0.0512 [ 0.0510; 0.0513] 0.0 1.5 Central and South America and the Caribbean
## EGYPT 0.1647 [ 0.1646; 0.1648] 0.3 1.5 Northern Africa
## ESTONIA 1.2831 [ 1.2799; 1.2862] 0.0 1.5 Northern Europe
## FINLAND 3.0971 [ 3.0947; 3.0996] 0.3 1.5 Northern Europe
## FRANCE 1.3516 [ 1.3511; 1.3521] 1.5 1.5 Western Europe
## GERMANY 1.6750 [ 1.6746; 1.6755] 2.5 1.5 Western Europe
## GREECE 0.9888 [ 0.9878; 0.9898] 0.2 1.5 Southern Europe
## HUNGARY 0.8107 [ 0.8098; 0.8117] 0.1 1.5 Eastern Europe
## INDIA 0.0244 [ 0.0244; 0.0245] 0.6 1.5 Southern Asia
## IRELAND 1.2545 [ 1.2528; 1.2562] 0.1 1.5 Northern Europe
## ITALY 0.5734 [ 0.5731; 0.5737] 0.6 1.5 Southern Europe
## JAPAN 0.0833 [ 0.0832; 0.0833] 0.2 1.5 Eastern Asia
## JORDAN 0.3329 [ 0.3323; 0.3335] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0263 [ 0.0262; 0.0265] 0.0 1.5 Central Asia
## KUWAIT 0.9165 [ 0.9150; 0.9180] 0.1 1.5 Western Asia
## LATVIA 2.3533 [ 2.3498; 2.3569] 0.1 1.5 Northern Europe
## LEBANON 0.4987 [ 0.4978; 0.4996] 0.1 1.5 Western Asia
## LITHUANIA 0.5957 [ 0.5942; 0.5972] 0.0 1.5 Northern Europe
## LUXEMBOURG 0.6434 [ 0.6400; 0.6467] 0.0 1.5 Western Europe
## MEXICO 0.1002 [ 0.1001; 0.1003] 0.2 1.5 Central and South America and the Caribbean
## MOROCCO 0.0402 [ 0.0401; 0.0403] 0.0 1.5 Northern Africa
## NETHERLANDS 0.8129 [ 0.8122; 0.8136] 0.2 1.5 Western Europe
## NEW ZEALAND 3.9910 [ 3.9880; 3.9939] 0.3 1.5 Australia and New Zealand
## NORWAY 3.1948 [ 3.1923; 3.1973] 0.3 1.5 Northern Europe
## PAKISTAN 0.0328 [ 0.0328; 0.0328] 0.1 1.5 Southern Asia
## PERU 0.0703 [ 0.0701; 0.0704] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0299 [ 0.0298; 0.0299] 0.1 1.5 South-eastern Asia
## POLAND 0.5082 [ 0.5079; 0.5086] 0.3 1.5 Eastern Europe
## PORTUGAL 1.4806 [ 1.4794; 1.4819] 0.3 1.5 Southern Europe
## PUERTO RICO 13.9247 [13.9178; 13.9317] 0.7 1.5 Central and South America and the Caribbean
## ROMANIA 1.3918 [ 1.3909; 1.3926] 0.5 1.5 Eastern Europe
## RUSSIA 0.2102 [ 0.2101; 0.2103] 0.5 1.5 Eastern Europe
## SAUDI ARABIA 0.7810 [ 0.7805; 0.7815] 0.5 1.5 Western Asia
## SERBIA 0.1066 [ 0.1063; 0.1070] 0.0 1.5 Southern Europe
## SLOVAKIA 1.7118 [ 1.7100; 1.7136] 0.2 1.5 Eastern Europe
## SLOVENIA 0.3713 [ 0.3700; 0.3727] 0.0 1.5 Southern Europe
## SOUTH AFRICA 0.0662 [ 0.0661; 0.0663] 0.1 1.5 Southern Africa
## SOUTH KOREA 0.9307 [ 0.9303; 0.9312] 0.8 1.5 Eastern Asia
## SPAIN 2.2219 [ 2.2212; 2.2226] 1.8 1.5 Southern Europe
## SWEDEN 4.4354 [ 4.4332; 4.4376] 0.8 1.5 Northern Europe
## SWITZERLAND 0.5148 [ 0.5141; 0.5156] 0.1 1.5 Western Europe
## TAIWAN 0.2588 [ 0.2585; 0.2592] 0.1 1.5 Eastern Asia
## THAILAND 0.7063 [ 0.7060; 0.7067] 0.9 1.5 South-eastern Asia
## TUNISIA 0.0909 [ 0.0906; 0.0911] 0.0 1.5 Northern Africa
## TÜRKIYE 2.5175 [ 2.5170; 2.5181] 3.7 1.5 Western Asia
## UNITED ARAB EMIRATES 0.0566 [ 0.0563; 0.0568] 0.0 1.5 Western Asia
## UNITED KINGDOM 6.1017 [ 6.1008; 6.1027] 7.2 1.5 Northern Europe
## UNITED STATES 11.6873 [11.6867; 11.6879] 67.4 1.5 Northern America
## URUGUAY 0.3050 [ 0.3040; 0.3059] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.0258 [ 0.0257; 0.0259] 0.0 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.2928 [6.2926; 6.2931] 83704.27 0
## Random effects model 0.4039 [0.2708; 0.6026] -4.44 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.7063 [1.8873; 6.0213]; tau = 1.6451 [1.3738; 2.4538]
## I^2 = 100.0%; H = 6850.89
##
## Test of heterogeneity:
## Q d.f. p-value
## 3003817304.32 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 11.2341 [11.2335; 11.2347] 48859543.91 100.0%
## Region = Central and South America and t ... 10 1.0038 [ 1.0034; 1.0041] 201390660.03 100.0%
## Region = Northern Europe 8 5.4451 [ 5.4443; 5.4459] 16583184.93 100.0%
## Region = Eastern Europe 8 0.7371 [ 0.7369; 0.7373] 35675656.15 100.0%
## Region = Southern Europe 8 1.4668 [ 1.4665; 1.4672] 23217524.82 100.0%
## Region = Western Europe 7 1.4629 [ 1.4627; 1.4632] 6481036.47 100.0%
## Region = Australia and New Zealand 2 1.7326 [ 1.7317; 1.7335] 10039724.38 100.0%
## Region = Eastern Asia 4 0.1682 [ 0.1681; 0.1682] 98898236.56 100.0%
## Region = Central Asia 1 0.0263 [ 0.0262; 0.0265] 0.00 --
## Region = South-eastern Asia 2 0.5824 [ 0.5822; 0.5827] 10930833.68 100.0%
## Region = Southern Asia 2 0.0257 [ 0.0257; 0.0257] 181860.89 100.0%
## Region = Western Asia 6 2.0610 [ 2.0606; 2.0614] 22109284.06 100.0%
## Region = Northern Africa 4 0.1321 [ 0.1320; 0.1322] 1295162.86 100.0%
## Region = Southern Africa 1 0.0662 [ 0.0661; 0.0663] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2528154595.57 13 0
## Within groups 475662708.75 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 7.5090 [3.1551; 17.8712] 0.3914 0.6257
## Region = Central and South America and t ... 10 0.1064 [0.0170; 0.6641] 8.7294 2.9546
## Region = Northern Europe 8 2.2266 [1.5437; 3.2116] 0.2794 0.5286
## Region = Eastern Europe 8 0.6224 [0.3121; 1.2412] 0.9925 0.9962
## Region = Southern Europe 8 0.4892 [0.2767; 0.8649] 0.6760 0.8222
## Region = Western Europe 7 1.0141 [0.7999; 1.2857] 0.1026 0.3204
## Region = Australia and New Zealand 2 1.6718 [0.3038; 9.2011] 1.5142 1.2305
## Region = Eastern Asia 4 0.1604 [0.0233; 1.1043] 3.8770 1.9690
## Region = Central Asia 1 0.0263 [0.0262; 0.0265] -- --
## Region = South-eastern Asia 2 0.1452 [0.0065; 3.2249] 5.0047 2.2371
## Region = Southern Asia 2 0.0283 [0.0212; 0.0378] 0.0433 0.2081
## Region = Western Asia 6 0.5067 [0.2356; 1.0897] 0.9159 0.9570
## Region = Northern Africa 4 0.0858 [0.0479; 0.1535] 0.3529 0.5940
## Region = Southern Africa 1 0.0662 [0.0661; 0.0663] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 134284.90 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0709 [0.0708; 0.0711] 0.2 1.8 Northern Africa
## ARGENTINA 0.1063 [0.1061; 0.1065] 0.3 1.8 Central and South America and the Caribbean
## AUSTRALIA 0.5278 [0.5272; 0.5283] 0.7 1.8 Australia and New Zealand
## AUSTRIA 0.7538 [0.7528; 0.7548] 0.4 1.8 Western Europe
## BELARUS 0.0007 [0.0007; 0.0008] 0.0 1.8 Eastern Europe
## BELGIUM 0.9523 [0.9513; 0.9533] 0.7 1.8 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0000 0.0 0.0 Central and South America and the Caribbean
## BULGARIA 0.0186 [0.0185; 0.0188] 0.0 1.8 Eastern Europe
## CANADA 1.6387 [1.6380; 1.6395] 3.6 1.8 Northern America
## CHILE 0.1689 [0.1685; 0.1692] 0.2 1.8 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0536 [0.0535; 0.0537] 0.2 1.8 Central and South America and the Caribbean
## CROATIA 0.0233 [0.0231; 0.0236] 0.0 1.8 Southern Europe
## CZECH REPUBLIC 0.4263 [0.4256; 0.4269] 0.3 1.8 Eastern Europe
## ECUADOR 0.1220 [0.1217; 0.1223] 0.1 1.8 Central and South America and the Caribbean
## EGYPT 0.0305 [0.0305; 0.0306] 0.2 1.8 Northern Africa
## ESTONIA 0.1266 [0.1256; 0.1276] 0.0 1.8 Northern Europe
## FINLAND 3.3965 [3.3939; 3.3991] 1.2 1.8 Northern Europe
## FRANCE 2.1673 [2.1667; 2.1679] 8.8 1.8 Western Europe
## GERMANY 1.5554 [1.5549; 1.5558] 8.3 1.8 Western Europe
## GREECE 1.4149 [1.4138; 1.4161] 1.0 1.8 Southern Europe
## HUNGARY 0.4953 [0.4946; 0.4961] 0.3 1.8 Eastern Europe
## INDIA 0.0441 [0.0441; 0.0441] 3.5 1.8 Southern Asia
## IRELAND 1.9534 [1.9512; 1.9556] 0.6 1.8 Northern Europe
## ITALY 0.9209 [0.9205; 0.9213] 3.6 1.8 Southern Europe
## JAPAN 0.0000 0.0 0.0 Eastern Asia
## JORDAN 0.0510 [0.0507; 0.0513] 0.0 1.8 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.0877 [0.0871; 0.0883] 0.0 1.8 Western Asia
## LATVIA 0.0843 [0.0837; 0.0849] 0.0 1.8 Northern Europe
## LEBANON 0.1605 [0.1599; 0.1611] 0.1 1.8 Western Asia
## LITHUANIA 0.1282 [0.1276; 0.1289] 0.0 1.8 Northern Europe
## LUXEMBOURG 2.3963 [2.3891; 2.4035] 0.1 1.8 Western Europe
## MEXICO 0.2075 [0.2074; 0.2077] 1.5 1.8 Central and South America and the Caribbean
## MOROCCO 0.0162 [0.0161; 0.0163] 0.0 1.8 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0120 [0.0118; 0.0121] 0.0 1.8 Australia and New Zealand
## NORWAY 2.3244 [2.3221; 2.3267] 0.7 1.8 Northern Europe
## PAKISTAN 0.0431 [0.0430; 0.0431] 0.5 1.8 Southern Asia
## PERU 0.0255 [0.0254; 0.0256] 0.0 1.8 Central and South America and the Caribbean
## PHILIPPINES 0.0270 [0.0269; 0.0270] 0.2 1.8 South-eastern Asia
## POLAND 0.0043 [0.0043; 0.0043] 0.0 1.8 Eastern Europe
## PORTUGAL 1.8127 [1.8114; 1.8140] 1.3 1.8 Southern Europe
## PUERTO RICO 1.2632 [1.2613; 1.2651] 0.3 1.8 Central and South America and the Caribbean
## ROMANIA 0.2512 [0.2508; 0.2515] 0.3 1.8 Eastern Europe
## RUSSIA 0.0137 [0.0137; 0.0138] 0.1 1.8 Eastern Europe
## SAUDI ARABIA 0.2557 [0.2553; 0.2560] 0.4 1.8 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 0.8130 [0.8117; 0.8143] 0.3 1.8 Eastern Europe
## SLOVENIA 1.0760 [1.0736; 1.0784] 0.1 1.8 Southern Europe
## SOUTH AFRICA 0.0644 [0.0643; 0.0645] 0.2 1.8 Southern Africa
## SOUTH KOREA 0.3052 [0.3049; 0.3055] 1.0 1.8 Eastern Asia
## SPAIN 2.0490 [2.0484; 2.0497] 6.2 1.8 Southern Europe
## SWEDEN 2.1903 [2.1887; 2.1919] 1.3 1.8 Northern Europe
## SWITZERLAND 1.3323 [1.3310; 1.3337] 0.7 1.8 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.0578 [0.0575; 0.0580] 0.0 1.8 Northern Africa
## TÜRKIYE 0.2052 [0.2050; 0.2053] 0.9 1.8 Western Asia
## UNITED ARAB EMIRATES 0.2101 [0.2095; 0.2106] 0.1 1.8 Western Asia
## UNITED KINGDOM 1.3586 [1.3581; 1.3591] 5.5 1.8 Northern Europe
## UNITED STATES 2.1871 [2.1868; 2.1873] 43.5 1.8 Northern America
## URUGUAY 0.0582 [0.0578; 0.0586] 0.0 1.8 Central and South America and the Caribbean
## VENEZUELA 0.2297 [0.2294; 0.2300] 0.4 1.8 Central and South America and the Caribbean
##
## Number of studies combined: k = 56
##
## rate 95%-CI z p-value
## Common effect model 1.3693 [1.3692; 1.3694] 7418.94 0
## Random effects model 0.2088 [0.1559; 0.2796] -10.50 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2456 [1.2763; 3.9522]; tau = 1.1161 [1.1297; 1.9880]
## I^2 = 100.0%; H = 3145.69
##
## Test of heterogeneity:
## Q d.f. p-value
## 544245166.16 55 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.1396 [2.1393; 2.1398] 1535997.93 100.0%
## Region = Central and South America and t ... 9 0.2054 [0.2053; 0.2055] 9246969.51 100.0%
## Region = Northern Europe 8 1.7157 [1.7152; 1.7162] 7664667.23 100.0%
## Region = Eastern Europe 8 0.3041 [0.3039; 0.3043] 10716904.43 100.0%
## Region = Southern Europe 6 1.5372 [1.5368; 1.5375] 9033186.47 100.0%
## Region = Western Europe 6 1.7501 [1.7498; 1.7505] 6227429.60 100.0%
## Region = Australia and New Zealand 2 0.5188 [0.5183; 0.5193] 266695.99 100.0%
## Region = Eastern Asia 1 0.3052 [0.3049; 0.3055] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0270 [0.0269; 0.0270] 0.00 --
## Region = Southern Asia 2 0.0440 [0.0440; 0.0440] 1314.98 99.9%
## Region = Western Asia 6 0.2107 [0.2106; 0.2109] 425970.07 100.0%
## Region = Northern Africa 4 0.0436 [0.0435; 0.0436] 526000.68 100.0%
## Region = Southern Africa 1 0.0644 [0.0643; 0.0645] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 498600029.26 12 0
## Within groups 45645136.90 43 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 1.8932 [1.4267; 2.5121] 0.0417 0.2041
## Region = Central and South America and t ... 9 0.1298 [0.0729; 0.2312] 0.7796 0.8830
## Region = Northern Europe 8 0.7075 [0.5034; 0.9946] 0.2414 0.4914
## Region = Eastern Europe 8 0.0494 [0.0198; 0.1232] 1.7419 1.3198
## Region = Southern Europe 6 0.7039 [0.4885; 1.0143] 0.2085 0.4566
## Region = Western Europe 6 1.4060 [1.0912; 1.8116] 0.1003 0.3168
## Region = Australia and New Zealand 2 0.0794 [0.0019; 3.2509] 7.1735 2.6783
## Region = Eastern Asia 1 0.3052 [0.3049; 0.3055] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0270 [0.0269; 0.0270] -- --
## Region = Southern Asia 2 0.0436 [0.0426; 0.0446] 0.0003 0.0166
## Region = Western Asia 6 0.1412 [0.1113; 0.1791] 0.0884 0.2973
## Region = Northern Africa 4 0.0377 [0.0208; 0.0684] 0.3684 0.6069
## Region = Southern Africa 1 0.0644 [0.0643; 0.0645] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 6007555.70 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.2753 [0.2750; 0.2756] 0.5 1.8 Northern Africa
## ARGENTINA 0.1721 [0.1719; 0.1723] 0.4 1.8 Central and South America and the Caribbean
## AUSTRALIA 0.7022 [0.7016; 0.7028] 0.9 1.8 Australia and New Zealand
## AUSTRIA 0.9630 [0.9619; 0.9641] 0.5 1.8 Western Europe
## BELARUS 0.0048 [0.0047; 0.0049] 0.0 1.8 Eastern Europe
## BELGIUM 1.2113 [1.2103; 1.2124] 0.7 1.8 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0149 [0.0149; 0.0149] 0.2 1.8 Central and South America and the Caribbean
## BULGARIA 0.1057 [0.1053; 0.1061] 0.0 1.8 Eastern Europe
## CANADA 2.1523 [2.1515; 2.1531] 4.1 1.8 Northern America
## CHILE 0.1928 [0.1924; 0.1931] 0.2 1.8 Central and South America and the Caribbean
## CHINA 0.0000 0.0 0.0 Eastern Asia
## COLOMBIA 0.0553 [0.0552; 0.0554] 0.1 1.8 Central and South America and the Caribbean
## CROATIA 0.0632 [0.0628; 0.0636] 0.0 1.8 Southern Europe
## CZECH REPUBLIC 0.5902 [0.5894; 0.5909] 0.3 1.8 Eastern Europe
## ECUADOR 0.1724 [0.1721; 0.1728] 0.1 1.8 Central and South America and the Caribbean
## EGYPT 0.0666 [0.0665; 0.0667] 0.3 1.8 Northern Africa
## ESTONIA 0.1453 [0.1442; 0.1463] 0.0 1.8 Northern Europe
## FINLAND 4.0030 [4.0002; 4.0058] 1.2 1.8 Northern Europe
## FRANCE 2.5553 [2.5547; 2.5560] 9.0 1.8 Western Europe
## GERMANY 1.8744 [1.8739; 1.8749] 8.5 1.8 Western Europe
## GREECE 1.8254 [1.8241; 1.8267] 1.1 1.8 Southern Europe
## HUNGARY 0.5623 [0.5615; 0.5631] 0.3 1.8 Eastern Europe
## INDIA 0.0531 [0.0530; 0.0531] 3.6 1.8 Southern Asia
## IRELAND 2.6032 [2.6008; 2.6057] 0.7 1.8 Northern Europe
## ITALY 1.1183 [1.1179; 1.1188] 3.7 1.8 Southern Europe
## JAPAN 0.0000 0.0 0.0 Eastern Asia
## JORDAN 0.0665 [0.0662; 0.0669] 0.0 1.8 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1249 [0.1242; 0.1256] 0.0 1.8 Western Asia
## LATVIA 0.1286 [0.1278; 0.1294] 0.0 1.8 Northern Europe
## LEBANON 0.2201 [0.2194; 0.2208] 0.1 1.8 Western Asia
## LITHUANIA 0.1815 [0.1807; 0.1823] 0.0 1.8 Northern Europe
## LUXEMBOURG 2.6071 [2.5997; 2.6145] 0.1 1.8 Western Europe
## MEXICO 0.2079 [0.2077; 0.2080] 1.3 1.8 Central and South America and the Caribbean
## MOROCCO 0.0335 [0.0334; 0.0336] 0.1 1.8 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0279 [0.0276; 0.0281] 0.0 1.8 Australia and New Zealand
## NORWAY 2.1852 [2.1830; 2.1873] 0.6 1.8 Northern Europe
## PAKISTAN 0.0910 [0.0909; 0.0911] 0.9 1.8 Southern Asia
## PERU 0.0375 [0.0374; 0.0376] 0.1 1.8 Central and South America and the Caribbean
## PHILIPPINES 0.0372 [0.0371; 0.0372] 0.2 1.8 South-eastern Asia
## POLAND 0.0071 [0.0070; 0.0071] 0.0 1.8 Eastern Europe
## PORTUGAL 2.2539 [2.2525; 2.2554] 1.3 1.8 Southern Europe
## PUERTO RICO 1.2524 [1.2505; 1.2543] 0.3 1.8 Central and South America and the Caribbean
## ROMANIA 0.4295 [0.4290; 0.4300] 0.5 1.8 Eastern Europe
## RUSSIA 0.0284 [0.0284; 0.0285] 0.2 1.8 Eastern Europe
## SAUDI ARABIA 0.3084 [0.3081; 0.3088] 0.5 1.8 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 1.1281 [1.1266; 1.1296] 0.3 1.8 Eastern Europe
## SLOVENIA 1.3641 [1.3615; 1.3668] 0.2 1.8 Southern Europe
## SOUTH AFRICA 0.1130 [0.1129; 0.1132] 0.3 1.8 Southern Africa
## SOUTH KOREA 0.4698 [0.4694; 0.4701] 1.3 1.8 Eastern Asia
## SPAIN 2.6137 [2.6129; 2.6145] 6.8 1.8 Southern Europe
## SWEDEN 2.7350 [2.7332; 2.7368] 1.4 1.8 Northern Europe
## SWITZERLAND 1.5463 [1.5449; 1.5478] 0.7 1.8 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.1176 [0.1172; 0.1179] 0.1 1.8 Northern Africa
## TÜRKIYE 0.2955 [0.2953; 0.2957] 1.2 1.8 Western Asia
## UNITED ARAB EMIRATES 0.3381 [0.3374; 0.3387] 0.2 1.8 Western Asia
## UNITED KINGDOM 1.8136 [1.8130; 1.8141] 6.4 1.8 Northern Europe
## UNITED STATES 2.2201 [2.2199; 2.2204] 38.1 1.8 Northern America
## URUGUAY 0.1756 [0.1748; 0.1763] 0.0 1.8 Central and South America and the Caribbean
## VENEZUELA 0.2484 [0.2480; 0.2487] 0.4 1.8 Central and South America and the Caribbean
##
## Number of studies combined: k = 57
##
## rate 95%-CI z p-value
## Common effect model 1.4606 [1.4604; 1.4607] 9668.73 0
## Random effects model 0.2979 [0.2233; 0.3976] -8.23 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2346 [1.2416; 3.6478]; tau = 1.1111 [1.1143; 1.9099]
## I^2 = 100.0%; H = 3441.39
##
## Test of heterogeneity:
## Q d.f. p-value
## 663216284.22 56 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.2135 [2.2132; 2.2138] 23087.78 100.0%
## Region = Central and South America and t ... 10 0.1878 [0.1877; 0.1879] 15387580.19 100.0%
## Region = Northern Europe 8 2.1468 [2.1462; 2.1473] 7515737.61 100.0%
## Region = Eastern Europe 8 0.3774 [0.3772; 0.3777] 15799897.69 100.0%
## Region = Southern Europe 6 1.9427 [1.9423; 1.9432] 12801029.89 100.0%
## Region = Western Europe 6 2.0833 [2.0829; 2.0836] 6629979.07 100.0%
## Region = Australia and New Zealand 2 0.6847 [0.6841; 0.6853] 454510.50 100.0%
## Region = Eastern Asia 1 0.4698 [0.4694; 0.4701] 0.00 --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0372 [0.0371; 0.0372] 0.00 --
## Region = Southern Asia 2 0.0590 [0.0590; 0.0591] 1360946.81 100.0%
## Region = Western Asia 6 0.2904 [0.2902; 0.2906] 520895.36 100.0%
## Region = Northern Africa 4 0.1467 [0.1466; 0.1468] 3512657.89 100.0%
## Region = Southern Africa 1 0.1130 [0.1129; 0.1132] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 599209961.42 12 0
## Within groups 64006322.79 44 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.1860 [2.1205; 2.2535] 0.0005 0.0219
## Region = Central and South America and t ... 10 0.1349 [0.0725; 0.2513] 1.0058 1.0029
## Region = Northern Europe 8 0.8870 [0.6538; 1.2033] 0.1937 0.4402
## Region = Eastern Europe 8 0.1064 [0.0432; 0.2619] 1.6912 1.3005
## Region = Southern Europe 6 1.0061 [0.6824; 1.4833] 0.2354 0.4852
## Region = Western Europe 6 1.6805 [1.3241; 2.1330] 0.0888 0.2979
## Region = Australia and New Zealand 2 0.1399 [0.0059; 3.3051] 5.2073 2.2820
## Region = Eastern Asia 1 0.4698 [0.4694; 0.4701] -- --
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0372 [0.0371; 0.0372] -- --
## Region = Southern Asia 2 0.0695 [0.0409; 0.1179] 0.1455 0.3814
## Region = Western Asia 6 0.1958 [0.1568; 0.2446] 0.0773 0.2780
## Region = Northern Africa 4 0.0922 [0.0356; 0.2389] 0.9445 0.9719
## Region = Southern Africa 1 0.1130 [0.1129; 0.1132] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 9115749.92 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.3584 [0.3581; 0.3587] 0.6 1.7 Northern Africa
## ARGENTINA 0.2832 [0.2829; 0.2835] 0.6 1.7 Central and South America and the Caribbean
## AUSTRALIA 0.8531 [0.8524; 0.8537] 0.9 1.7 Australia and New Zealand
## AUSTRIA 1.2971 [1.2959; 1.2984] 0.5 1.7 Western Europe
## BELARUS 0.0057 [0.0056; 0.0058] 0.0 1.7 Eastern Europe
## BELGIUM 1.7617 [1.7604; 1.7630] 0.9 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0598 [0.0597; 0.0598] 0.6 1.7 Central and South America and the Caribbean
## BULGARIA 0.1647 [0.1642; 0.1652] 0.1 1.7 Eastern Europe
## CANADA 2.5654 [2.5645; 2.5663] 4.3 1.7 Northern America
## CHILE 0.2310 [0.2307; 0.2314] 0.2 1.7 Central and South America and the Caribbean
## CHINA 0.0000 [0.0000; 0.0000] 0.0 1.7 Eastern Asia
## COLOMBIA 0.0525 [0.0524; 0.0526] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.1874 [0.1868; 0.1881] 0.0 1.7 Southern Europe
## CZECH REPUBLIC 0.7723 [0.7714; 0.7731] 0.4 1.7 Eastern Europe
## ECUADOR 0.2196 [0.2193; 0.2200] 0.2 1.7 Central and South America and the Caribbean
## EGYPT 0.1329 [0.1327; 0.1330] 0.5 1.7 Northern Africa
## ESTONIA 0.1949 [0.1936; 0.1961] 0.0 1.7 Northern Europe
## FINLAND 4.4528 [4.4499; 4.4558] 1.2 1.7 Northern Europe
## FRANCE 2.9223 [2.9216; 2.9230] 9.0 1.7 Western Europe
## GERMANY 2.1889 [2.1883; 2.1894] 8.7 1.7 Western Europe
## GREECE 2.0220 [2.0206; 2.0234] 1.1 1.7 Southern Europe
## HUNGARY 0.6597 [0.6589; 0.6605] 0.3 1.7 Eastern Europe
## INDIA 0.0606 [0.0605; 0.0606] 3.7 1.7 Southern Asia
## IRELAND 3.0900 [3.0873; 3.0926] 0.7 1.7 Northern Europe
## ITALY 1.3033 [1.3028; 1.3037] 3.8 1.7 Southern Europe
## JAPAN 0.1340 [0.1339; 0.1341] 0.8 1.7 Eastern Asia
## JORDAN 0.0748 [0.0744; 0.0751] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1322 [0.1315; 0.1329] 0.0 1.7 Western Asia
## LATVIA 0.0932 [0.0925; 0.0939] 0.0 1.7 Northern Europe
## LEBANON 0.2822 [0.2814; 0.2830] 0.1 1.7 Western Asia
## LITHUANIA 0.2761 [0.2751; 0.2770] 0.0 1.7 Northern Europe
## LUXEMBOURG 2.8977 [2.8900; 2.9055] 0.1 1.7 Western Europe
## MEXICO 0.2123 [0.2122; 0.2124] 1.2 1.7 Central and South America and the Caribbean
## MOROCCO 0.0372 [0.0371; 0.0373] 0.1 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.0433 [0.0429; 0.0436] 0.0 1.7 Australia and New Zealand
## NORWAY 2.1474 [2.1452; 2.1495] 0.5 1.7 Northern Europe
## PAKISTAN 0.1127 [0.1127; 0.1128] 1.0 1.7 Southern Asia
## PERU 0.0528 [0.0527; 0.0530] 0.1 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0396 [0.0395; 0.0397] 0.2 1.7 South-eastern Asia
## POLAND 0.0090 [0.0089; 0.0090] 0.0 1.7 Eastern Europe
## PORTUGAL 2.6513 [2.6496; 2.6529] 1.4 1.7 Southern Europe
## PUERTO RICO 1.2272 [1.2253; 1.2291] 0.2 1.7 Central and South America and the Caribbean
## ROMANIA 0.3411 [0.3407; 0.3415] 0.3 1.7 Eastern Europe
## RUSSIA 0.0725 [0.0724; 0.0725] 0.5 1.7 Eastern Europe
## SAUDI ARABIA 0.3637 [0.3633; 0.3641] 0.5 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 1.4980 [1.4963; 1.4997] 0.4 1.7 Eastern Europe
## SLOVENIA 1.6411 [1.6382; 1.6440] 0.2 1.7 Southern Europe
## SOUTH AFRICA 0.1369 [0.1367; 0.1371] 0.3 1.7 Southern Africa
## SOUTH KOREA 0.5801 [0.5797; 0.5804] 1.4 1.7 Eastern Asia
## SPAIN 3.0765 [3.0757; 3.0773] 7.1 1.7 Southern Europe
## SWEDEN 3.0492 [3.0473; 3.0510] 1.4 1.7 Northern Europe
## SWITZERLAND 1.8991 [1.8975; 1.9007] 0.7 1.7 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.0669 [0.0668; 0.0670] 0.2 1.7 South-eastern Asia
## TUNISIA 0.1599 [0.1595; 0.1603] 0.1 1.7 Northern Africa
## TÜRKIYE 0.3880 [0.3878; 0.3883] 1.4 1.7 Western Asia
## UNITED ARAB EMIRATES 0.6148 [0.6140; 0.6157] 0.3 1.7 Western Asia
## UNITED KINGDOM 2.3866 [2.3860; 2.3873] 7.4 1.7 Northern Europe
## UNITED STATES 2.1808 [2.1805; 2.1811] 33.1 1.7 Northern America
## URUGUAY 0.3082 [0.3073; 0.3092] 0.1 1.7 Central and South America and the Caribbean
## VENEZUELA 0.2893 [0.2890; 0.2896] 0.4 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 1.4998 [1.4997; 1.4999] 11053.78 0
## Random effects model 0.3117 [0.2336; 0.4158] -7.93 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2975 [1.2802; 3.5669]; tau = 1.1391 [1.1315; 1.8886]
## I^2 = 100.0%; H = 3745.29
##
## Test of heterogeneity:
## Q d.f. p-value
## 827603287.42 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.2219 [2.2217; 2.2222] 746916.15 100.0%
## Region = Central and South America and t ... 10 0.1958 [0.1957; 0.1959] 15110283.34 100.0%
## Region = Northern Europe 8 2.6183 [2.6177; 2.6188] 6506270.94 100.0%
## Region = Eastern Europe 8 0.3797 [0.3795; 0.3799] 20664536.04 100.0%
## Region = Southern Europe 6 2.2675 [2.2670; 2.2679] 15896797.72 100.0%
## Region = Western Europe 6 2.4238 [2.4235; 2.4242] 5632704.75 100.0%
## Region = Australia and New Zealand 2 0.8282 [0.8276; 0.8289] 607742.82 100.0%
## Region = Eastern Asia 3 0.3322 [0.3320; 0.3323] 9759332.54 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.0528 [0.0527; 0.0528] 204889.73 100.0%
## Region = Southern Asia 2 0.0691 [0.0691; 0.0692] 2244377.16 100.0%
## Region = Western Asia 6 0.3881 [0.3879; 0.3883] 1178972.07 100.0%
## Region = Northern Africa 4 0.2047 [0.2045; 0.2048] 3543122.42 100.0%
## Region = Southern Africa 1 0.1369 [0.1367; 0.1371] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 745507341.75 12 0
## Within groups 82095945.67 47 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.3653 [2.0172; 2.7734] 0.0132 0.1148
## Region = Central and South America and t ... 10 0.1881 [0.1118; 0.3164] 0.7042 0.8392
## Region = Northern Europe 8 1.0095 [0.7752; 1.3145] 0.1452 0.3811
## Region = Eastern Europe 8 0.1414 [0.0577; 0.3464] 1.6734 1.2936
## Region = Southern Europe 6 1.3700 [0.9183; 2.0439] 0.2500 0.5000
## Region = Western Europe 6 2.0777 [1.7002; 2.5390] 0.0628 0.2506
## Region = Australia and New Zealand 2 0.1921 [0.0103; 3.5687] 4.4450 2.1083
## Region = Eastern Asia 3 0.0133 [0.0038; 0.0469] 1.2367 1.1121
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.0515 [0.0308; 0.0861] 0.1377 0.3711
## Region = Southern Asia 2 0.0826 [0.0450; 0.1519] 0.1930 0.4393
## Region = Western Asia 6 0.2496 [0.1878; 0.3318] 0.1264 0.3556
## Region = Northern Africa 4 0.1297 [0.0604; 0.2788] 0.6096 0.7808
## Region = Southern Africa 1 0.1369 [0.1367; 0.1371] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 2333.16 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.5023 [0.5019; 0.5026] 0.8 1.6 Northern Africa
## ARGENTINA 0.4871 [0.4868; 0.4875] 0.8 1.6 Central and South America and the Caribbean
## AUSTRALIA 1.0422 [1.0415; 1.0429] 1.0 1.6 Australia and New Zealand
## AUSTRIA 1.6069 [1.6055; 1.6083] 0.6 1.6 Western Europe
## BELARUS 0.0049 [0.0049; 0.0050] 0.0 1.6 Eastern Europe
## BELGIUM 2.1459 [2.1444; 2.1473] 1.0 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.0047 [0.0046; 0.0048] 0.0 1.6 Southern Europe
## BRAZIL 0.0849 [0.0848; 0.0849] 0.7 1.6 Central and South America and the Caribbean
## BULGARIA 0.2681 [0.2675; 0.2687] 0.1 1.6 Eastern Europe
## CANADA 2.8489 [2.8480; 2.8498] 4.1 1.6 Northern America
## CHILE 0.2907 [0.2903; 0.2912] 0.2 1.6 Central and South America and the Caribbean
## CHINA 0.0006 [0.0006; 0.0006] 0.0 1.6 Eastern Asia
## COLOMBIA 0.0601 [0.0600; 0.0602] 0.1 1.6 Central and South America and the Caribbean
## CROATIA 0.2490 [0.2483; 0.2498] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.0325 [1.0315; 1.0335] 0.5 1.6 Eastern Europe
## ECUADOR 0.2650 [0.2645; 0.2654] 0.2 1.6 Central and South America and the Caribbean
## EGYPT 0.2101 [0.2100; 0.2103] 0.7 1.6 Northern Africa
## ESTONIA 0.2309 [0.2295; 0.2322] 0.0 1.6 Northern Europe
## FINLAND 4.7455 [4.7425; 4.7485] 1.1 1.6 Northern Europe
## FRANCE 3.1118 [3.1111; 3.1125] 8.2 1.6 Western Europe
## GERMANY 2.3903 [2.3897; 2.3909] 8.0 1.6 Western Europe
## GREECE 2.1845 [2.1831; 2.1860] 1.0 1.6 Southern Europe
## HUNGARY 0.7808 [0.7799; 0.7817] 0.3 1.6 Eastern Europe
## INDIA 0.0673 [0.0672; 0.0673] 3.5 1.6 Southern Asia
## IRELAND 3.7947 [3.7917; 3.7976] 0.7 1.6 Northern Europe
## ITALY 1.4936 [1.4931; 1.4942] 3.7 1.6 Southern Europe
## JAPAN 0.9483 [0.9481; 0.9486] 5.1 1.6 Eastern Asia
## JORDAN 0.0969 [0.0965; 0.0972] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0096 [0.0095; 0.0097] 0.0 1.6 Central Asia
## KUWAIT 0.1977 [0.1969; 0.1985] 0.0 1.6 Western Asia
## LATVIA 0.0698 [0.0692; 0.0703] 0.0 1.6 Northern Europe
## LEBANON 0.4274 [0.4265; 0.4283] 0.1 1.6 Western Asia
## LITHUANIA 0.4855 [0.4842; 0.4868] 0.1 1.6 Northern Europe
## LUXEMBOURG 3.0533 [3.0455; 3.0612] 0.1 1.6 Western Europe
## MEXICO 0.1896 [0.1894; 0.1897] 0.9 1.6 Central and South America and the Caribbean
## MOROCCO 0.0429 [0.0428; 0.0430] 0.1 1.6 Northern Africa
## NETHERLANDS 1.8776 [1.8765; 1.8787] 1.3 1.6 Western Europe
## NEW ZEALAND 0.0559 [0.0555; 0.0562] 0.0 1.6 Australia and New Zealand
## NORWAY 2.4446 [2.4424; 2.4469] 0.5 1.6 Northern Europe
## PAKISTAN 0.1482 [0.1481; 0.1483] 1.1 1.6 Southern Asia
## PERU 0.0621 [0.0619; 0.0622] 0.1 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0455 [0.0455; 0.0456] 0.2 1.6 South-eastern Asia
## POLAND 0.0169 [0.0169; 0.0170] 0.0 1.6 Eastern Europe
## PORTUGAL 2.7607 [2.7590; 2.7623] 1.2 1.6 Southern Europe
## PUERTO RICO 1.2731 [1.2712; 1.2750] 0.2 1.6 Central and South America and the Caribbean
## ROMANIA 0.3141 [0.3137; 0.3145] 0.3 1.6 Eastern Europe
## RUSSIA 0.1415 [0.1414; 0.1416] 0.8 1.6 Eastern Europe
## SAUDI ARABIA 0.4245 [0.4241; 0.4249] 0.5 1.6 Western Asia
## SERBIA 0.0287 [0.0285; 0.0289] 0.0 1.6 Southern Europe
## SLOVAKIA 2.0042 [2.0022; 2.0062] 0.5 1.6 Eastern Europe
## SLOVENIA 1.8618 [1.8587; 1.8649] 0.2 1.6 Southern Europe
## SOUTH AFRICA 0.1591 [0.1589; 0.1592] 0.3 1.6 Southern Africa
## SOUTH KOREA 0.6952 [0.6948; 0.6956] 1.4 1.6 Eastern Asia
## SPAIN 3.5436 [3.5427; 3.5445] 6.9 1.6 Southern Europe
## SWEDEN 3.2395 [3.2376; 3.2413] 1.3 1.6 Northern Europe
## SWITZERLAND 2.1983 [2.1966; 2.2000] 0.7 1.6 Western Europe
## TAIWAN 0.0000 0.0 0.0 Eastern Asia
## THAILAND 0.1031 [0.1030; 0.1033] 0.3 1.6 South-eastern Asia
## TUNISIA 0.1955 [0.1950; 0.1959] 0.1 1.6 Northern Africa
## TÜRKIYE 0.5242 [0.5240; 0.5245] 1.6 1.6 Western Asia
## UNITED ARAB EMIRATES 0.6845 [0.6836; 0.6854] 0.3 1.6 Western Asia
## UNITED KINGDOM 2.9403 [2.9396; 2.9409] 7.8 1.6 Northern Europe
## UNITED STATES 2.1866 [2.1863; 2.1868] 28.3 1.6 Northern America
## URUGUAY 0.3948 [0.3937; 0.3959] 0.1 1.6 Central and South America and the Caribbean
## VENEZUELA 0.3710 [0.3706; 0.3713] 0.4 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 1.5664 [1.5663; 1.5665] 13307.14 0
## Random effects model 0.3546 [0.2720; 0.4623] -7.66 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1715 [1.2291; 3.2801]; tau = 1.0824 [1.1087; 1.8111]
## I^2 = 100.0%; H = 3807.60
##
## Test of heterogeneity:
## Q d.f. p-value
## 913361648.43 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.2608 [2.2606; 2.2611] 2198773.01 100.0%
## Region = Central and South America and t ... 10 0.2390 [0.2390; 0.2391] 19435163.65 100.0%
## Region = Northern Europe 8 3.0866 [3.0860; 3.0872] 5817963.39 100.0%
## Region = Eastern Europe 8 0.4525 [0.4523; 0.4527] 25771185.22 100.0%
## Region = Southern Europe 8 2.5580 [2.5576; 2.5585] 20575019.80 100.0%
## Region = Western Europe 7 2.5736 [2.5732; 2.5740] 5678033.59 100.0%
## Region = Australia and New Zealand 2 1.0110 [1.0103; 1.0117] 763923.00 100.0%
## Region = Eastern Asia 3 0.8538 [0.8536; 0.8541] 16206503.66 100.0%
## Region = Central Asia 1 0.0096 [0.0095; 0.0097] 0.00 --
## Region = South-eastern Asia 2 0.0753 [0.0752; 0.0754] 653532.18 100.0%
## Region = Southern Asia 2 0.0816 [0.0816; 0.0816] 4682244.42 100.0%
## Region = Western Asia 6 0.4970 [0.4968; 0.4972] 1315843.83 100.0%
## Region = Northern Africa 4 0.2965 [0.2963; 0.2966] 4690223.83 100.0%
## Region = Southern Africa 1 0.1591 [0.1589; 0.1592] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 805573238.86 13 0
## Within groups 107788409.57 50 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.4959 [1.9258; 3.2347] 0.0350 0.1871
## Region = Central and South America and t ... 10 0.2303 [0.1360; 0.3899] 0.7213 0.8493
## Region = Northern Europe 8 1.1601 [0.9184; 1.4654] 0.1137 0.3372
## Region = Eastern Europe 8 0.1887 [0.0800; 0.4450] 1.5324 1.2379
## Region = Southern Europe 8 0.4602 [0.3172; 0.6677] 0.2883 0.5370
## Region = Western Europe 7 2.2830 [1.9361; 2.6920] 0.0495 0.2225
## Region = Australia and New Zealand 2 0.2413 [0.0137; 4.2460] 4.2818 2.0692
## Region = Eastern Asia 3 0.0719 [0.0261; 0.1982] 0.8040 0.8967
## Region = Central Asia 1 0.0096 [0.0095; 0.0097] -- --
## Region = South-eastern Asia 2 0.0685 [0.0308; 0.1527] 0.3340 0.5780
## Region = Southern Asia 2 0.0999 [0.0460; 0.2166] 0.3121 0.5586
## Region = Western Asia 6 0.3281 [0.2513; 0.4282] 0.1109 0.3330
## Region = Northern Africa 4 0.1725 [0.0830; 0.3585] 0.5577 0.7468
## Region = Southern Africa 1 0.1591 [0.1589; 0.1592] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 449063.97 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.5436 [0.5432; 0.5440] 0.7 1.5 Northern Africa
## ARGENTINA 0.6692 [0.6688; 0.6696] 1.0 1.5 Central and South America and the Caribbean
## AUSTRALIA 1.1969 [1.1962; 1.1977] 1.0 1.5 Australia and New Zealand
## AUSTRIA 1.9266 [1.9250; 1.9281] 0.6 1.5 Western Europe
## BELARUS 0.0061 [0.0060; 0.0062] 0.0 1.5 Eastern Europe
## BELGIUM 2.2474 [2.2459; 2.2489] 0.9 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0084 [0.0082; 0.0085] 0.0 1.5 Southern Europe
## BRAZIL 0.1127 [0.1126; 0.1128] 0.8 1.5 Central and South America and the Caribbean
## BULGARIA 0.0647 [0.0644; 0.0650] 0.0 1.5 Eastern Europe
## CANADA 3.1446 [3.1436; 3.1456] 4.0 1.5 Northern America
## CHILE 0.3808 [0.3804; 0.3813] 0.2 1.5 Central and South America and the Caribbean
## CHINA 0.0017 [0.0017; 0.0017] 0.1 1.5 Eastern Asia
## COLOMBIA 0.0711 [0.0710; 0.0712] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.3248 [0.3239; 0.3257] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 1.1044 [1.1034; 1.1055] 0.4 1.5 Eastern Europe
## ECUADOR 0.3125 [0.3120; 0.3129] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 0.3476 [0.3474; 0.3478] 1.1 1.5 Northern Africa
## ESTONIA 0.2598 [0.2584; 0.2613] 0.0 1.5 Northern Europe
## FINLAND 5.0579 [5.0548; 5.0610] 1.0 1.5 Northern Europe
## FRANCE 3.4227 [3.4219; 3.4234] 8.0 1.5 Western Europe
## GERMANY 2.6294 [2.6288; 2.6300] 7.8 1.5 Western Europe
## GREECE 2.1222 [2.1208; 2.1237] 0.8 1.5 Southern Europe
## HUNGARY 0.9178 [0.9168; 0.9188] 0.3 1.5 Eastern Europe
## INDIA 0.0698 [0.0698; 0.0699] 3.2 1.5 Southern Asia
## IRELAND 4.8273 [4.8240; 4.8306] 0.8 1.5 Northern Europe
## ITALY 1.6178 [1.6172; 1.6183] 3.6 1.5 Southern Europe
## JAPAN 1.6550 [1.6546; 1.6554] 7.8 1.5 Eastern Asia
## JORDAN 0.1713 [0.1709; 0.1718] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0219 [0.0218; 0.0220] 0.0 1.5 Central Asia
## KUWAIT 0.3129 [0.3119; 0.3139] 0.0 1.5 Western Asia
## LATVIA 0.0739 [0.0733; 0.0745] 0.0 1.5 Northern Europe
## LEBANON 0.4941 [0.4931; 0.4951] 0.1 1.5 Western Asia
## LITHUANIA 0.5840 [0.5826; 0.5855] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.3108 [3.3027; 3.3189] 0.1 1.5 Western Europe
## MEXICO 0.1834 [0.1833; 0.1835] 0.8 1.5 Central and South America and the Caribbean
## MOROCCO 0.0481 [0.0479; 0.0482] 0.1 1.5 Northern Africa
## NETHERLANDS 2.1870 [2.1858; 2.1881] 1.3 1.5 Western Europe
## NEW ZEALAND 0.0606 [0.0602; 0.0610] 0.0 1.5 Australia and New Zealand
## NORWAY 2.6430 [2.6406; 2.6454] 0.5 1.5 Northern Europe
## PAKISTAN 0.1655 [0.1654; 0.1656] 1.1 1.5 Southern Asia
## PERU 0.0764 [0.0762; 0.0766] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0513 [0.0512; 0.0513] 0.2 1.5 South-eastern Asia
## POLAND 0.0124 [0.0124; 0.0125] 0.0 1.5 Eastern Europe
## PORTUGAL 3.1609 [3.1591; 3.1627] 1.2 1.5 Southern Europe
## PUERTO RICO 1.2744 [1.2724; 1.2763] 0.2 1.5 Central and South America and the Caribbean
## ROMANIA 0.2308 [0.2305; 0.2312] 0.2 1.5 Eastern Europe
## RUSSIA 0.2983 [0.2982; 0.2985] 1.6 1.5 Eastern Europe
## SAUDI ARABIA 0.6099 [0.6094; 0.6103] 0.7 1.5 Western Asia
## SERBIA 0.0514 [0.0512; 0.0517] 0.0 1.5 Southern Europe
## SLOVAKIA 2.1634 [2.1614; 2.1655] 0.4 1.5 Eastern Europe
## SLOVENIA 2.0907 [2.0875; 2.0940] 0.2 1.5 Southern Europe
## SOUTH AFRICA 0.1847 [0.1845; 0.1849] 0.4 1.5 Southern Africa
## SOUTH KOREA 1.0076 [1.0072; 1.0081] 1.9 1.5 Eastern Asia
## SPAIN 3.9095 [3.9085; 3.9104] 6.7 1.5 Southern Europe
## SWEDEN 3.2022 [3.2003; 3.2040] 1.1 1.5 Northern Europe
## SWITZERLAND 2.4428 [2.4410; 2.4445] 0.7 1.5 Western Europe
## TAIWAN 0.0082 [0.0082; 0.0083] 0.0 1.5 Eastern Asia
## THAILAND 0.1173 [0.1172; 0.1175] 0.3 1.5 South-eastern Asia
## TUNISIA 0.2953 [0.2948; 0.2959] 0.1 1.5 Northern Africa
## TÜRKIYE 0.7695 [0.7692; 0.7699] 2.1 1.5 Western Asia
## UNITED ARAB EMIRATES 0.2766 [0.2760; 0.2772] 0.1 1.5 Western Asia
## UNITED KINGDOM 3.4816 [3.4808; 3.4823] 8.2 1.5 Northern Europe
## UNITED STATES 2.0969 [2.0967; 2.0972] 24.2 1.5 Northern America
## URUGUAY 0.6758 [0.6743; 0.6772] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.4633 [0.4629; 0.4637] 0.5 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.6762 [1.6761; 1.6763] 16299.08 0
## Random effects model 0.3943 [0.3052; 0.5095] -7.12 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1102 [1.2216; 3.2212]; tau = 1.0537 [1.1053; 1.7948]
## I^2 = 100.0%; H = 3953.76
##
## Test of heterogeneity:
## Q d.f. p-value
## 1000461830.41 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.2220 [2.2217; 2.2222] 5644374.52 100.0%
## Region = Central and South America and t ... 10 0.2948 [0.2948; 0.2949] 25414502.83 100.0%
## Region = Northern Europe 8 3.5544 [3.5538; 3.5551] 6403590.09 100.0%
## Region = Eastern Europe 8 0.5198 [0.5196; 0.5200] 21741294.72 100.0%
## Region = Southern Europe 8 2.7975 [2.7970; 2.7980] 24509070.32 100.0%
## Region = Western Europe 7 2.8386 [2.8382; 2.8390] 5721498.51 100.0%
## Region = Australia and New Zealand 2 1.1625 [1.1618; 1.1632] 871567.40 100.0%
## Region = Eastern Asia 4 1.4099 [1.4097; 1.4102] 45374333.24 100.0%
## Region = Central Asia 1 0.0219 [0.0218; 0.0220] 0.00 --
## Region = South-eastern Asia 2 0.0853 [0.0852; 0.0854] 767198.47 100.0%
## Region = Southern Asia 2 0.0874 [0.0873; 0.0874] 6239538.17 100.0%
## Region = Western Asia 6 0.6743 [0.6740; 0.6745] 2437697.91 100.0%
## Region = Northern Africa 4 0.3833 [0.3832; 0.3835] 3606048.41 100.0%
## Region = Southern Africa 1 0.1847 [0.1845; 0.1849] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 851731115.82 13 0
## Within groups 148730714.58 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.5679 [1.7263; 3.8197] 0.0821 0.2865
## Region = Central and South America and t ... 10 0.2853 [0.1657; 0.4910] 0.7678 0.8763
## Region = Northern Europe 8 1.2983 [1.0287; 1.6384] 0.1128 0.3358
## Region = Eastern Europe 8 0.1713 [0.0826; 0.3554] 1.1091 1.0531
## Region = Southern Europe 8 0.5776 [0.3913; 0.8527] 0.3159 0.5621
## Region = Western Europe 7 2.5438 [2.1748; 2.9754] 0.0448 0.2116
## Region = Australia and New Zealand 2 0.2693 [0.0145; 5.0114] 4.4501 2.1095
## Region = Eastern Asia 4 0.0699 [0.0215; 0.2272] 1.4464 1.2027
## Region = Central Asia 1 0.0219 [0.0218; 0.0220] -- --
## Region = South-eastern Asia 2 0.0776 [0.0345; 0.1746] 0.3427 0.5854
## Region = Southern Asia 2 0.1075 [0.0462; 0.2504] 0.3722 0.6101
## Region = Western Asia 6 0.3885 [0.2792; 0.5407] 0.1706 0.4131
## Region = Northern Africa 4 0.2276 [0.1310; 0.3953] 0.3176 0.5636
## Region = Southern Africa 1 0.1847 [0.1845; 0.1849] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 588727.22 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.6123 [0.6119; 0.6128] 0.7 1.5 Northern Africa
## ARGENTINA 0.8307 [0.8302; 0.8311] 1.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 3.1224 [3.1212; 3.1236] 2.3 1.5 Australia and New Zealand
## AUSTRIA 2.1375 [2.1358; 2.1391] 0.6 1.5 Western Europe
## BELARUS 0.0123 [0.0122; 0.0124] 0.0 1.5 Eastern Europe
## BELGIUM 2.0611 [2.0597; 2.0625] 0.7 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0126 [0.0124; 0.0128] 0.0 1.5 Southern Europe
## BRAZIL 0.1486 [0.1485; 0.1487] 0.9 1.5 Central and South America and the Caribbean
## BULGARIA 0.0809 [0.0806; 0.0813] 0.0 1.5 Eastern Europe
## CANADA 3.7305 [3.7294; 3.7315] 4.1 1.5 Northern America
## CHILE 0.4682 [0.4676; 0.4687] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0024 [0.0024; 0.0024] 0.1 1.5 Eastern Asia
## COLOMBIA 0.0892 [0.0891; 0.0893] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.3720 [0.3710; 0.3730] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 1.3551 [1.3539; 1.3562] 0.4 1.5 Eastern Europe
## ECUADOR 0.3316 [0.3311; 0.3320] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 0.4690 [0.4688; 0.4693] 1.3 1.5 Northern Africa
## ESTONIA 0.3397 [0.3381; 0.3414] 0.0 1.5 Northern Europe
## FINLAND 5.3754 [5.3721; 5.3786] 0.9 1.5 Northern Europe
## FRANCE 3.6856 [3.6849; 3.6864] 7.4 1.5 Western Europe
## GERMANY 2.8213 [2.8207; 2.8219] 7.2 1.5 Western Europe
## GREECE 2.3245 [2.3230; 2.3260] 0.8 1.5 Southern Europe
## HUNGARY 1.0685 [1.0674; 1.0696] 0.3 1.5 Eastern Europe
## INDIA 0.0717 [0.0717; 0.0718] 2.9 1.5 Southern Asia
## IRELAND 6.0138 [6.0100; 6.0175] 0.9 1.5 Northern Europe
## ITALY 1.7605 [1.7600; 1.7611] 3.3 1.5 Southern Europe
## JAPAN 2.1922 [2.1917; 2.1926] 8.8 1.5 Eastern Asia
## JORDAN 0.3112 [0.3106; 0.3118] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0286 [0.0284; 0.0287] 0.0 1.5 Central Asia
## KUWAIT 0.6898 [0.6884; 0.6913] 0.1 1.5 Western Asia
## LATVIA 0.0938 [0.0931; 0.0945] 0.0 1.5 Northern Europe
## LEBANON 0.5122 [0.5112; 0.5131] 0.1 1.5 Western Asia
## LITHUANIA 0.7074 [0.7058; 0.7089] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.3751 [3.3670; 3.3832] 0.1 1.5 Western Europe
## MEXICO 0.1822 [0.1821; 0.1823] 0.7 1.5 Central and South America and the Caribbean
## MOROCCO 0.0512 [0.0510; 0.0513] 0.1 1.5 Northern Africa
## NETHERLANDS 2.4227 [2.4214; 2.4239] 1.3 1.5 Western Europe
## NEW ZEALAND 0.0756 [0.0752; 0.0760] 0.0 1.5 Australia and New Zealand
## NORWAY 2.7219 [2.7196; 2.7243] 0.4 1.5 Northern Europe
## PAKISTAN 0.1842 [0.1841; 0.1843] 1.1 1.5 Southern Asia
## PERU 0.0920 [0.0918; 0.0922] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0583 [0.0583; 0.0584] 0.2 1.5 South-eastern Asia
## POLAND 0.0159 [0.0159; 0.0160] 0.0 1.5 Eastern Europe
## PORTUGAL 3.1361 [3.1343; 3.1379] 1.0 1.5 Southern Europe
## PUERTO RICO 1.2648 [1.2629; 1.2668] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.1968 [0.1965; 0.1972] 0.1 1.5 Eastern Europe
## RUSSIA 0.6995 [0.6993; 0.6997] 3.2 1.5 Eastern Europe
## SAUDI ARABIA 0.9389 [0.9383; 0.9395] 0.9 1.5 Western Asia
## SERBIA 0.1583 [0.1578; 0.1587] 0.0 1.5 Southern Europe
## SLOVAKIA 2.2623 [2.2602; 2.2644] 0.4 1.5 Eastern Europe
## SLOVENIA 2.2580 [2.2546; 2.2614] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.1957 [0.1955; 0.1959] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.1367 [1.1362; 1.1372] 1.8 1.5 Eastern Asia
## SPAIN 4.2439 [4.2429; 4.2448] 6.2 1.5 Southern Europe
## SWEDEN 3.2839 [3.2820; 3.2858] 1.0 1.5 Northern Europe
## SWITZERLAND 2.6708 [2.6689; 2.6726] 0.7 1.5 Western Europe
## TAIWAN 0.0939 [0.0937; 0.0941] 0.1 1.5 Eastern Asia
## THAILAND 0.1231 [0.1230; 0.1233] 0.3 1.5 South-eastern Asia
## TUNISIA 0.3557 [0.3552; 0.3563] 0.1 1.5 Northern Africa
## TÜRKIYE 1.0282 [1.0278; 1.0286] 2.4 1.5 Western Asia
## UNITED ARAB EMIRATES 0.4835 [0.4828; 0.4843] 0.1 1.5 Western Asia
## UNITED KINGDOM 4.2010 [4.2002; 4.2019] 8.6 1.5 Northern Europe
## UNITED STATES 2.2311 [2.2308; 2.2313] 22.1 1.5 Northern America
## URUGUAY 0.7672 [0.7657; 0.7688] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.6107 [0.6102; 0.6111] 0.6 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.8841 [1.8840; 1.8842] 21624.21 0
## Random effects model 0.5042 [0.3934; 0.6462] -5.41 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0417 [1.1631; 3.0301]; tau = 1.0207 [1.0785; 1.7407]
## I^2 = 100.0%; H = 4163.34
##
## Test of heterogeneity:
## Q d.f. p-value
## 1109335495.37 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.4188 [2.4186; 2.4191] 10711405.87 100.0%
## Region = Central and South America and t ... 10 0.3532 [0.3531; 0.3533] 31126765.26 100.0%
## Region = Northern Europe 8 4.1760 [4.1753; 4.1767] 8114577.22 100.0%
## Region = Eastern Europe 8 0.8002 [0.7999; 0.8004] 15403456.19 100.0%
## Region = Southern Europe 8 2.9893 [2.9888; 2.9898] 27978586.73 100.0%
## Region = Western Europe 7 3.0454 [3.0449; 3.0458] 6660558.83 100.0%
## Region = Australia and New Zealand 2 3.0685 [3.0673; 3.0696] 1719042.83 100.0%
## Region = Eastern Asia 4 1.8043 [1.8040; 1.8046] 68292960.79 100.0%
## Region = Central Asia 1 0.0286 [0.0284; 0.0287] 0.00 --
## Region = South-eastern Asia 2 0.0908 [0.0908; 0.0909] 696449.66 100.0%
## Region = Southern Asia 2 0.0932 [0.0931; 0.0932] 8272656.43 100.0%
## Region = Western Asia 6 0.9281 [0.9279; 0.9284] 2616505.68 100.0%
## Region = Northern Africa 4 0.4780 [0.4778; 0.4781] 3799056.18 100.0%
## Region = Southern Africa 1 0.1957 [0.1955; 0.1959] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 923943473.69 13 0
## Within groups 185392021.68 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 2.8849 [1.7432; 4.7745] 0.1321 0.3635
## Region = Central and South America and t ... 10 0.3334 [0.1927; 0.5769] 0.7825 0.8846
## Region = Northern Europe 8 1.5123 [1.1803; 1.9378] 0.1279 0.3577
## Region = Eastern Europe 8 0.2275 [0.1323; 0.3912] 0.6122 0.7824
## Region = Southern Europe 8 0.7412 [0.4963; 1.1069] 0.3350 0.5788
## Region = Western Europe 7 2.6828 [2.2788; 3.1586] 0.0486 0.2204
## Region = Australia and New Zealand 2 0.4858 [0.0127; 18.6292] 6.9238 2.6313
## Region = Eastern Asia 4 0.1533 [0.0413; 0.5690] 1.7904 1.3381
## Region = Central Asia 1 0.0286 [0.0284; 0.0287] -- --
## Region = South-eastern Asia 2 0.0848 [0.0408; 0.1762] 0.2789 0.5281
## Region = Southern Asia 2 0.1150 [0.0456; 0.2897] 0.4447 0.6668
## Region = Western Asia 6 0.6096 [0.4629; 0.8028] 0.1184 0.3441
## Region = Northern Africa 4 0.2689 [0.1611; 0.4489] 0.2735 0.5230
## Region = Southern Africa 1 0.1957 [0.1955; 0.1959] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 630303.81 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.8746 [0.8741; 0.8750] 0.9 1.5 Northern Africa
## ARGENTINA 0.9517 [0.9512; 0.9522] 1.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 5.4618 [5.4602; 5.4633] 3.5 1.5 Australia and New Zealand
## AUSTRIA 2.3277 [2.3261; 2.3294] 0.5 1.5 Western Europe
## BELARUS 0.0146 [0.0145; 0.0147] 0.0 1.5 Eastern Europe
## BELGIUM 2.0963 [2.0949; 2.0978] 0.6 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0180 [0.0177; 0.0182] 0.0 1.5 Southern Europe
## BRAZIL 0.1909 [0.1908; 0.1910] 1.1 1.5 Central and South America and the Caribbean
## BULGARIA 0.1039 [0.1035; 0.1043] 0.0 1.5 Eastern Europe
## CANADA 4.2330 [4.2318; 4.2341] 4.1 1.5 Northern America
## CHILE 0.5657 [0.5651; 0.5663] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0026 [0.0026; 0.0026] 0.1 1.5 Eastern Asia
## COLOMBIA 0.1081 [0.1080; 0.1083] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.4368 [0.4358; 0.4379] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 1.5417 [1.5405; 1.5430] 0.4 1.5 Eastern Europe
## ECUADOR 0.3778 [0.3774; 0.3783] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 0.6020 [0.6018; 0.6023] 1.5 1.5 Northern Africa
## ESTONIA 0.4430 [0.4412; 0.4449] 0.0 1.5 Northern Europe
## FINLAND 4.9036 [4.9005; 4.9067] 0.7 1.5 Northern Europe
## FRANCE 4.0148 [4.0140; 4.0156] 7.0 1.5 Western Europe
## GERMANY 3.0256 [3.0250; 3.0263] 6.7 1.5 Western Europe
## GREECE 2.3194 [2.3179; 2.3209] 0.7 1.5 Southern Europe
## HUNGARY 1.1791 [1.1780; 1.1803] 0.3 1.5 Eastern Europe
## INDIA 0.0766 [0.0766; 0.0766] 2.7 1.5 Southern Asia
## IRELAND 6.5956 [6.5917; 6.5995] 0.8 1.5 Northern Europe
## ITALY 1.9060 [1.9054; 1.9065] 3.1 1.5 Southern Europe
## JAPAN 2.7970 [2.7965; 2.7974] 9.7 1.5 Eastern Asia
## JORDAN 0.4901 [0.4893; 0.4909] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0399 [0.0398; 0.0401] 0.0 1.5 Central Asia
## KUWAIT 0.5753 [0.5740; 0.5766] 0.1 1.5 Western Asia
## LATVIA 0.1005 [0.0998; 0.1013] 0.0 1.5 Northern Europe
## LEBANON 0.5807 [0.5797; 0.5817] 0.1 1.5 Western Asia
## LITHUANIA 0.8691 [0.8674; 0.8709] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.5380 [3.5298; 3.5462] 0.1 1.5 Western Europe
## MEXICO 0.2060 [0.2059; 0.2062] 0.7 1.5 Central and South America and the Caribbean
## MOROCCO 0.0548 [0.0547; 0.0550] 0.1 1.5 Northern Africa
## NETHERLANDS 2.6728 [2.6715; 2.6741] 1.2 1.5 Western Europe
## NEW ZEALAND 0.0869 [0.0864; 0.0873] 0.0 1.5 Australia and New Zealand
## NORWAY 2.8620 [2.8596; 2.8645] 0.4 1.5 Northern Europe
## PAKISTAN 0.2062 [0.2061; 0.2063] 1.1 1.5 Southern Asia
## PERU 0.1047 [0.1045; 0.1049] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0678 [0.0678; 0.0679] 0.2 1.5 South-eastern Asia
## POLAND 0.0388 [0.0387; 0.0389] 0.0 1.5 Eastern Europe
## PORTUGAL 3.6144 [3.6125; 3.6163] 1.0 1.5 Southern Europe
## PUERTO RICO 1.2472 [1.2452; 1.2491] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.1747 [0.1744; 0.1750] 0.1 1.5 Eastern Europe
## RUSSIA 0.9011 [0.9009; 0.9014] 3.5 1.5 Eastern Europe
## SAUDI ARABIA 1.2680 [1.2673; 1.2687] 1.1 1.5 Western Asia
## SERBIA 0.3061 [0.3055; 0.3067] 0.1 1.5 Southern Europe
## SLOVAKIA 2.3157 [2.3136; 2.3179] 0.3 1.5 Eastern Europe
## SLOVENIA 2.6181 [2.6145; 2.6218] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2145 [0.2143; 0.2147] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.2171 [1.2166; 1.2176] 1.7 1.5 Eastern Asia
## SPAIN 4.4807 [4.4797; 4.4817] 5.7 1.5 Southern Europe
## SWEDEN 3.2563 [3.2544; 3.2582] 0.9 1.5 Northern Europe
## SWITZERLAND 2.9188 [2.9169; 2.9207] 0.7 1.5 Western Europe
## TAIWAN 0.1689 [0.1687; 0.1692] 0.1 1.5 Eastern Asia
## THAILAND 0.1475 [0.1474; 0.1477] 0.3 1.5 South-eastern Asia
## TUNISIA 0.3975 [0.3969; 0.3982] 0.1 1.5 Northern Africa
## TÜRKIYE 1.5190 [1.5185; 1.5194] 3.2 1.5 Western Asia
## UNITED ARAB EMIRATES 0.7207 [0.7198; 0.7216] 0.2 1.5 Western Asia
## UNITED KINGDOM 5.0509 [5.0500; 5.0518] 9.0 1.5 Northern Europe
## UNITED STATES 2.3340 [2.3338; 2.3343] 20.2 1.5 Northern America
## URUGUAY 0.9316 [0.9299; 0.9333] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.7561 [0.7556; 0.7566] 0.6 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.1325 [2.1324; 2.1327] 27754.66 0
## Random effects model 0.5987 [0.4682; 0.7654] -4.09 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0216 [1.1325; 2.9345]; tau = 1.0107 [1.0642; 1.7130]
## I^2 = 100.0%; H = 4441.49
##
## Test of heterogeneity:
## Q d.f. p-value
## 1262517087.33 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.5807 [2.5804; 2.5809] 16243650.56 100.0%
## Region = Central and South America and t ... 10 0.4113 [0.4112; 0.4114] 34727583.30 100.0%
## Region = Northern Europe 8 4.8073 [4.8065; 4.8080] 9689188.04 100.0%
## Region = Eastern Europe 8 0.9599 [0.9596; 0.9601] 16775840.94 100.0%
## Region = Southern Europe 8 3.1679 [3.1674; 3.1684] 29961138.78 100.0%
## Region = Western Europe 7 3.2955 [3.2950; 3.2959] 7825704.39 100.0%
## Region = Australia and New Zealand 2 5.3928 [5.3912; 5.3943] 2477763.94 100.0%
## Region = Eastern Asia 4 2.2796 [2.2793; 2.2800] 84529640.98 100.0%
## Region = Central Asia 1 0.0399 [0.0398; 0.0401] 0.00 --
## Region = South-eastern Asia 2 0.1079 [0.1078; 0.1080] 897197.16 100.0%
## Region = Southern Asia 2 0.1020 [0.1019; 0.1020] 10253819.46 100.0%
## Region = Western Asia 6 1.3338 [1.3334; 1.3341] 4746950.32 100.0%
## Region = Northern Africa 4 0.6441 [0.6439; 0.6443] 5783925.88 100.0%
## Region = Southern Africa 1 0.2145 [0.2143; 0.2147] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1038604683.59 13 0
## Within groups 223912403.75 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.1432 [1.7539; 5.6329] 0.1772 0.4209
## Region = Central and South America and t ... 10 0.3891 [0.2292; 0.6606] 0.7295 0.8541
## Region = Northern Europe 8 1.6646 [1.2769; 2.1701] 0.1464 0.3826
## Region = Eastern Europe 8 0.2811 [0.1648; 0.4796] 0.5944 0.7710
## Region = Southern Europe 8 0.9052 [0.6077; 1.3483] 0.3306 0.5750
## Region = Western Europe 7 2.8782 [2.4286; 3.4110] 0.0526 0.2293
## Region = Australia and New Zealand 2 0.6889 [0.0119; 39.8600] 8.5739 2.9281
## Region = Eastern Asia 4 0.1959 [0.0497; 0.7720] 1.9583 1.3994
## Region = Central Asia 1 0.0399 [0.0398; 0.0401] -- --
## Region = South-eastern Asia 2 0.1000 [0.0467; 0.2142] 0.3017 0.5493
## Region = Southern Asia 2 0.1257 [0.0476; 0.3316] 0.4900 0.7000
## Region = Western Asia 6 0.7812 [0.5708; 1.0691] 0.1537 0.3921
## Region = Northern Africa 4 0.3273 [0.1896; 0.5651] 0.3105 0.5572
## Region = Southern Africa 1 0.2145 [0.2143; 0.2147] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 675004.03 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 1.0163 [1.0158; 1.0168] 1.0 1.5 Northern Africa
## ARGENTINA 1.1057 [1.1052; 1.1062] 1.2 1.5 Central and South America and the Caribbean
## AUSTRALIA 6.8676 [6.8659; 6.8693] 4.1 1.5 Australia and New Zealand
## AUSTRIA 2.4571 [2.4554; 2.4588] 0.5 1.5 Western Europe
## BELARUS 0.0133 [0.0132; 0.0134] 0.0 1.5 Eastern Europe
## BELGIUM 2.3686 [2.3671; 2.3700] 0.7 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0204 [0.0202; 0.0207] 0.0 1.5 Southern Europe
## BRAZIL 0.2552 [0.2551; 0.2553] 1.3 1.5 Central and South America and the Caribbean
## BULGARIA 0.1449 [0.1444; 0.1453] 0.0 1.5 Eastern Europe
## CANADA 4.8298 [4.8286; 4.8310] 4.3 1.5 Northern America
## CHILE 0.6778 [0.6772; 0.6785] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0034 [0.0034; 0.0034] 0.1 1.5 Eastern Asia
## COLOMBIA 0.1500 [0.1498; 0.1502] 0.2 1.5 Central and South America and the Caribbean
## CROATIA 0.5667 [0.5655; 0.5679] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 2.0048 [2.0034; 2.0062] 0.5 1.5 Eastern Europe
## ECUADOR 0.4372 [0.4367; 0.4377] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 0.8155 [0.8152; 0.8158] 1.9 1.5 Northern Africa
## ESTONIA 0.6044 [0.6022; 0.6066] 0.0 1.5 Northern Europe
## FINLAND 4.7511 [4.7480; 4.7541] 0.6 1.5 Northern Europe
## FRANCE 4.2238 [4.2230; 4.2247] 6.7 1.5 Western Europe
## GERMANY 3.2835 [3.2828; 3.2841] 6.6 1.5 Western Europe
## GREECE 2.3751 [2.3736; 2.3766] 0.6 1.5 Southern Europe
## HUNGARY 1.1061 [1.1050; 1.1072] 0.3 1.5 Eastern Europe
## INDIA 0.0808 [0.0808; 0.0808] 2.6 1.5 Southern Asia
## IRELAND 8.2475 [8.2432; 8.2519] 0.9 1.5 Northern Europe
## ITALY 1.9980 [1.9974; 1.9986] 3.0 1.5 Southern Europe
## JAPAN 3.0553 [3.0548; 3.0558] 9.7 1.5 Eastern Asia
## JORDAN 0.5068 [0.5060; 0.5075] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0641 [0.0639; 0.0643] 0.0 1.5 Central Asia
## KUWAIT 0.6329 [0.6316; 0.6342] 0.1 1.5 Western Asia
## LATVIA 0.0874 [0.0867; 0.0881] 0.0 1.5 Northern Europe
## LEBANON 0.6932 [0.6922; 0.6943] 0.1 1.5 Western Asia
## LITHUANIA 0.9057 [0.9039; 0.9075] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.4906 [3.4825; 3.4986] 0.0 1.5 Western Europe
## MEXICO 0.2250 [0.2249; 0.2252] 0.7 1.5 Central and South America and the Caribbean
## MOROCCO 0.0612 [0.0611; 0.0613] 0.1 1.5 Northern Africa
## NETHERLANDS 2.8345 [2.8332; 2.8359] 1.2 1.5 Western Europe
## NEW ZEALAND 0.1001 [0.0997; 0.1006] 0.0 1.5 Australia and New Zealand
## NORWAY 3.0177 [3.0153; 3.0202] 0.4 1.5 Northern Europe
## PAKISTAN 0.2419 [0.2418; 0.2420] 1.2 1.5 Southern Asia
## PERU 0.1435 [0.1433; 0.1437] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0781 [0.0780; 0.0782] 0.2 1.5 South-eastern Asia
## POLAND 0.0669 [0.0667; 0.0670] 0.1 1.5 Eastern Europe
## PORTUGAL 3.7551 [3.7532; 3.7571] 1.0 1.5 Southern Europe
## PUERTO RICO 1.1861 [1.1842; 1.1881] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.1684 [0.1681; 0.1687] 0.1 1.5 Eastern Europe
## RUSSIA 0.7960 [0.7958; 0.7963] 2.8 1.5 Eastern Europe
## SAUDI ARABIA 1.0275 [1.0269; 1.0281] 0.8 1.5 Western Asia
## SERBIA 0.6683 [0.6674; 0.6692] 0.1 1.5 Southern Europe
## SLOVAKIA 2.5507 [2.5485; 2.5529] 0.3 1.5 Eastern Europe
## SLOVENIA 2.9286 [2.9248; 2.9325] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2358 [0.2356; 0.2360] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.3430 [1.3425; 1.3435] 1.7 1.5 Eastern Asia
## SPAIN 4.4954 [4.4944; 4.4964] 5.2 1.5 Southern Europe
## SWEDEN 3.2796 [3.2777; 3.2815] 0.8 1.5 Northern Europe
## SWITZERLAND 3.2163 [3.2143; 3.2183] 0.7 1.5 Western Europe
## TAIWAN 0.2086 [0.2083; 0.2089] 0.1 1.5 Eastern Asia
## THAILAND 0.1601 [0.1599; 0.1602] 0.3 1.5 South-eastern Asia
## TUNISIA 0.4702 [0.4696; 0.4709] 0.1 1.5 Northern Africa
## TÜRKIYE 2.0758 [2.0753; 2.0763] 4.0 1.5 Western Asia
## UNITED ARAB EMIRATES 0.9940 [0.9929; 0.9951] 0.2 1.5 Western Asia
## UNITED KINGDOM 5.8870 [5.8860; 5.8879] 9.6 1.5 Northern Europe
## UNITED STATES 2.4324 [2.4321; 2.4326] 19.3 1.5 Northern America
## URUGUAY 1.1259 [1.1241; 1.1278] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.7361 [0.7356; 0.7366] 0.5 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.3234 [2.3233; 2.3235] 32424.33 0
## Random effects model 0.6843 [0.5331; 0.8783] -2.98 0.0029
##
## Quantifying heterogeneity:
## tau^2 = 1.0543 [1.1237; 2.8903]; tau = 1.0268 [1.0600; 1.7001]
## I^2 = 100.0%; H = 4743.32
##
## Test of heterogeneity:
## Q d.f. p-value
## 1439939034.97 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.7564 [2.7561; 2.7566] 24452429.19 100.0%
## Region = Central and South America and t ... 10 0.4609 [0.4607; 0.4610] 35451580.52 100.0%
## Region = Northern Europe 8 5.5532 [5.5524; 5.5540] 13999343.22 100.0%
## Region = Eastern Europe 8 0.9263 [0.9261; 0.9266] 23034030.61 100.0%
## Region = Southern Europe 8 3.1895 [3.1890; 3.1900] 28456519.38 100.0%
## Region = Western Europe 7 3.5193 [3.5188; 3.5197] 7219721.84 100.0%
## Region = Australia and New Zealand 2 6.7867 [6.7850; 6.7884] 3008527.49 100.0%
## Region = Eastern Asia 4 2.4581 [2.4577; 2.4585] 102850269.35 100.0%
## Region = Central Asia 1 0.0641 [0.0639; 0.0643] 0.00 --
## Region = South-eastern Asia 2 0.1184 [0.1183; 0.1185] 868951.56 100.0%
## Region = Southern Asia 2 0.1139 [0.1138; 0.1139] 14560800.37 100.0%
## Region = Western Asia 6 1.6926 [1.6923; 1.6930] 11068535.09 100.0%
## Region = Northern Africa 4 0.8188 [0.8186; 0.8191] 6492242.32 100.0%
## Region = Southern Africa 1 0.2358 [0.2356; 0.2360] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1168476084.05 13 0
## Within groups 271462950.92 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.4275 [1.7500; 6.7130] 0.2353 0.4850
## Region = Central and South America and t ... 10 0.4571 [0.2791; 0.7486] 0.6334 0.7958
## Region = Northern Europe 8 1.7980 [1.3256; 2.4388] 0.1935 0.4399
## Region = Eastern Europe 8 0.3154 [0.1734; 0.5735] 0.7446 0.8629
## Region = Southern Europe 8 1.0774 [0.7377; 1.5734] 0.2987 0.5465
## Region = Western Europe 7 3.0697 [2.6234; 3.5919] 0.0450 0.2121
## Region = Australia and New Zealand 2 0.8293 [0.0132; 52.2592] 8.9383 2.9897
## Region = Eastern Asia 4 0.2326 [0.0559; 0.9683] 2.1177 1.4552
## Region = Central Asia 1 0.0641 [0.0639; 0.0643] -- --
## Region = South-eastern Asia 2 0.1118 [0.0554; 0.2259] 0.2573 0.5072
## Region = Southern Asia 2 0.1398 [0.0477; 0.4095] 0.6015 0.7756
## Region = Western Asia 6 0.8822 [0.5519; 1.4102] 0.3437 0.5862
## Region = Northern Africa 4 0.3930 [0.2340; 0.6600] 0.2800 0.5291
## Region = Southern Africa 1 0.2358 [0.2356; 0.2360] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 644104.23 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 1.3989 [1.3983; 1.3995] 1.3 1.5 Northern Africa
## ARGENTINA 1.1449 [1.1443; 1.1454] 1.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 8.0026 [8.0007; 8.0044] 4.4 1.5 Australia and New Zealand
## AUSTRIA 3.4469 [3.4449; 3.4489] 0.7 1.5 Western Europe
## BELARUS 0.0210 [0.0209; 0.0212] 0.0 1.5 Eastern Europe
## BELGIUM 3.6912 [3.6894; 3.6931] 1.0 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0389 [0.0386; 0.0393] 0.0 1.5 Southern Europe
## BRAZIL 0.3278 [0.3277; 0.3280] 1.5 1.5 Central and South America and the Caribbean
## BULGARIA 0.4235 [0.4227; 0.4243] 0.1 1.5 Eastern Europe
## CANADA 5.4479 [5.4466; 5.4491] 4.5 1.5 Northern America
## CHILE 0.7712 [0.7705; 0.7719] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0048 [0.0048; 0.0048] 0.2 1.5 Eastern Asia
## COLOMBIA 0.1520 [0.1518; 0.1521] 0.2 1.5 Central and South America and the Caribbean
## CROATIA 0.9388 [0.9372; 0.9403] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 2.9635 [2.9618; 2.9652] 0.7 1.5 Eastern Europe
## ECUADOR 0.4496 [0.4491; 0.4501] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 1.1842 [1.1839; 1.1846] 2.5 1.5 Northern Africa
## ESTONIA 0.8538 [0.8512; 0.8564] 0.0 1.5 Northern Europe
## FINLAND 4.9233 [4.9203; 4.9264] 0.6 1.5 Northern Europe
## FRANCE 4.3282 [4.3274; 4.3290] 6.4 1.5 Western Europe
## GERMANY 3.5834 [3.5827; 3.5840] 6.7 1.5 Western Europe
## GREECE 2.9066 [2.9049; 2.9083] 0.7 1.5 Southern Europe
## HUNGARY 1.1861 [1.1849; 1.1872] 0.3 1.5 Eastern Europe
## INDIA 0.0826 [0.0826; 0.0827] 2.5 1.5 Southern Asia
## IRELAND 8.8947 [8.8902; 8.8992] 0.9 1.5 Northern Europe
## ITALY 2.0556 [2.0550; 2.0562] 2.8 1.5 Southern Europe
## JAPAN 3.3683 [3.3678; 3.3688] 9.8 1.5 Eastern Asia
## JORDAN 0.8144 [0.8134; 0.8153] 0.2 1.5 Western Asia
## KAZAKHSTAN 0.0473 [0.0471; 0.0474] 0.0 1.5 Central Asia
## KUWAIT 0.8609 [0.8594; 0.8624] 0.1 1.5 Western Asia
## LATVIA 0.1269 [0.1260; 0.1277] 0.0 1.5 Northern Europe
## LEBANON 0.8859 [0.8847; 0.8870] 0.1 1.5 Western Asia
## LITHUANIA 1.2196 [1.2174; 1.2217] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.6592 [3.6511; 3.6674] 0.0 1.5 Western Europe
## MEXICO 0.2381 [0.2379; 0.2382] 0.7 1.5 Central and South America and the Caribbean
## MOROCCO 0.0617 [0.0615; 0.0618] 0.0 1.5 Northern Africa
## NETHERLANDS 3.0032 [3.0018; 3.0045] 1.2 1.5 Western Europe
## NEW ZEALAND 0.1140 [0.1135; 0.1145] 0.0 1.5 Australia and New Zealand
## NORWAY 3.1358 [3.1333; 3.1384] 0.4 1.5 Northern Europe
## PAKISTAN 0.2771 [0.2770; 0.2773] 1.3 1.5 Southern Asia
## PERU 0.1565 [0.1563; 0.1567] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0931 [0.0930; 0.0932] 0.2 1.5 South-eastern Asia
## POLAND 0.3348 [0.3345; 0.3352] 0.3 1.5 Eastern Europe
## PORTUGAL 3.5745 [3.5725; 3.5764] 0.8 1.5 Southern Europe
## PUERTO RICO 1.1569 [1.1550; 1.1589] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.1887 [0.1883; 0.1890] 0.1 1.5 Eastern Europe
## RUSSIA 0.1847 [0.1846; 0.1849] 0.6 1.5 Eastern Europe
## SAUDI ARABIA 0.8962 [0.8957; 0.8968] 0.7 1.5 Western Asia
## SERBIA 1.0159 [1.0148; 1.0170] 0.2 1.5 Southern Europe
## SLOVAKIA 3.5712 [3.5686; 3.5739] 0.4 1.5 Eastern Europe
## SLOVENIA 3.3094 [3.3053; 3.3135] 0.2 1.5 Southern Europe
## SOUTH AFRICA 0.2596 [0.2594; 0.2599] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.5523 [1.5517; 1.5529] 1.8 1.5 Eastern Asia
## SPAIN 4.7605 [4.7595; 4.7615] 5.0 1.5 Southern Europe
## SWEDEN 3.2710 [3.2692; 3.2729] 0.7 1.5 Northern Europe
## SWITZERLAND 3.4599 [3.4578; 3.4619] 0.7 1.5 Western Europe
## TAIWAN 0.2559 [0.2555; 0.2562] 0.1 1.5 Eastern Asia
## THAILAND 0.1710 [0.1709; 0.1712] 0.3 1.5 South-eastern Asia
## TUNISIA 0.5303 [0.5296; 0.5310] 0.1 1.5 Northern Africa
## TÜRKIYE 2.5278 [2.5272; 2.5284] 4.6 1.5 Western Asia
## UNITED ARAB EMIRATES 0.7037 [0.7028; 0.7046] 0.1 1.5 Western Asia
## UNITED KINGDOM 6.7927 [6.7917; 6.7938] 10.2 1.5 Northern Europe
## UNITED STATES 2.4771 [2.4768; 2.4773] 18.2 1.5 Northern America
## URUGUAY 1.1942 [1.1923; 1.1961] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.5555 [0.5550; 0.5559] 0.4 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.5771 [2.5770; 2.5773] 37946.55 0
## Random effects model 0.8006 [0.6206; 1.0328] -1.71 0.0870
##
## Quantifying heterogeneity:
## tau^2 = 1.0978 [1.1543; 2.9411]; tau = 1.0477 [1.0744; 1.7150]
## I^2 = 100.0%; H = 5050.88
##
## Test of heterogeneity:
## Q d.f. p-value
## 1632730148.26 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 2.8966 [2.8963; 2.8969] 36042624.04 100.0%
## Region = Central and South America and t ... 10 0.4771 [0.4770; 0.4772] 31972062.34 100.0%
## Region = Northern Europe 8 6.3006 [6.2998; 6.3015] 17829890.73 100.0%
## Region = Eastern Europe 8 0.9297 [0.9295; 0.9300] 64190322.55 100.0%
## Region = Southern Europe 8 3.3399 [3.3394; 3.3404] 29127522.72 100.0%
## Region = Western Europe 7 3.8010 [3.8005; 3.8014] 3345905.84 100.0%
## Region = Australia and New Zealand 2 7.9103 [7.9085; 7.9121] 3496443.04 100.0%
## Region = Eastern Asia 4 2.6703 [2.6699; 2.6707] 128046083.35 100.0%
## Region = Central Asia 1 0.0473 [0.0471; 0.0474] 0.00 --
## Region = South-eastern Asia 2 0.1301 [0.1300; 0.1302] 716809.15 100.0%
## Region = Southern Asia 2 0.1247 [0.1247; 0.1248] 19916478.76 100.0%
## Region = Western Asia 6 2.0184 [2.0180; 2.0188] 18122311.48 100.0%
## Region = Northern Africa 4 1.1724 [1.1721; 1.1727] 8891027.38 100.0%
## Region = Southern Africa 1 0.2596 [0.2594; 0.2599] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1271032666.89 13 0
## Within groups 361697481.37 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.6735 [1.6968; 7.9528] 0.3106 0.5573
## Region = Central and South America and t ... 10 0.4734 [0.3006; 0.7454] 0.5366 0.7325
## Region = Northern Europe 8 2.1168 [1.5180; 2.9519] 0.2303 0.4799
## Region = Eastern Europe 8 0.4359 [0.1627; 1.1677] 2.0222 1.4220
## Region = Southern Europe 8 1.3711 [0.9469; 1.9852] 0.2853 0.5342
## Region = Western Europe 7 3.5777 [3.2343; 3.9576] 0.0186 0.1362
## Region = Australia and New Zealand 2 0.9551 [0.0148; 61.5841] 9.0376 3.0063
## Region = Eastern Asia 4 0.2829 [0.0650; 1.2319] 2.2534 1.5011
## Region = Central Asia 1 0.0473 [0.0471; 0.0474] -- --
## Region = South-eastern Asia 2 0.1262 [0.0696; 0.2290] 0.1848 0.4299
## Region = Southern Asia 2 0.1513 [0.0462; 0.4954] 0.7322 0.8557
## Region = Western Asia 6 0.9983 [0.5525; 1.8040] 0.5468 0.7395
## Region = Northern Africa 4 0.4825 [0.2877; 0.8089] 0.2782 0.5274
## Region = Southern Africa 1 0.2596 [0.2594; 0.2599] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 847720.12 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 2.9210 [2.9202; 2.9219] 2.5 1.5 Northern Africa
## ARGENTINA 1.2095 [1.2090; 1.2101] 1.1 1.5 Central and South America and the Caribbean
## AUSTRALIA 8.6417 [8.6397; 8.6436] 4.3 1.5 Australia and New Zealand
## AUSTRIA 3.6534 [3.6513; 3.6555] 0.7 1.5 Western Europe
## BELARUS 0.0391 [0.0389; 0.0393] 0.0 1.5 Eastern Europe
## BELGIUM 4.3393 [4.3373; 4.3413] 1.0 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0747 [0.0742; 0.0752] 0.0 1.5 Southern Europe
## BRAZIL 0.4008 [0.4007; 0.4010] 1.7 1.5 Central and South America and the Caribbean
## BULGARIA 0.6955 [0.6945; 0.6965] 0.1 1.5 Eastern Europe
## CANADA 6.0241 [6.0228; 6.0254] 4.5 1.5 Northern America
## CHILE 0.8596 [0.8589; 0.8603] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0065 [0.0065; 0.0066] 0.2 1.5 Eastern Asia
## COLOMBIA 0.1610 [0.1608; 0.1612] 0.2 1.5 Central and South America and the Caribbean
## CROATIA 1.1064 [1.1047; 1.1081] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 3.5544 [3.5525; 3.5563] 0.8 1.5 Eastern Europe
## ECUADOR 0.4751 [0.4746; 0.4757] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 1.9074 [1.9069; 1.9078] 3.7 1.5 Northern Africa
## ESTONIA 1.1673 [1.1643; 1.1704] 0.0 1.5 Northern Europe
## FINLAND 5.0831 [5.0800; 5.0862] 0.6 1.5 Northern Europe
## FRANCE 4.4210 [4.4201; 4.4218] 5.8 1.5 Western Europe
## GERMANY 3.7208 [3.7201; 3.7215] 6.3 1.5 Western Europe
## GREECE 3.2721 [3.2703; 3.2740] 0.7 1.5 Southern Europe
## HUNGARY 1.2721 [1.2710; 1.2733] 0.3 1.5 Eastern Europe
## INDIA 0.0859 [0.0858; 0.0859] 2.3 1.5 Southern Asia
## IRELAND 8.9632 [8.9587; 8.9676] 0.9 1.5 Northern Europe
## ITALY 2.2635 [2.2629; 2.2641] 2.8 1.5 Southern Europe
## JAPAN 3.7304 [3.7298; 3.7310] 9.7 1.5 Eastern Asia
## JORDAN 1.0108 [1.0097; 1.0118] 0.2 1.5 Western Asia
## KAZAKHSTAN 0.0662 [0.0660; 0.0664] 0.0 1.5 Central Asia
## KUWAIT 1.7104 [1.7083; 1.7125] 0.1 1.5 Western Asia
## LATVIA 0.1982 [0.1971; 0.1992] 0.0 1.5 Northern Europe
## LEBANON 0.9871 [0.9858; 0.9883] 0.1 1.5 Western Asia
## LITHUANIA 1.1829 [1.1808; 1.1850] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.8108 [3.8026; 3.8190] 0.0 1.5 Western Europe
## MEXICO 0.2819 [0.2817; 0.2820] 0.7 1.5 Central and South America and the Caribbean
## MOROCCO 0.1094 [0.1092; 0.1095] 0.1 1.5 Northern Africa
## NETHERLANDS 3.0973 [3.0959; 3.0987] 1.1 1.5 Western Europe
## NEW ZEALAND 0.1255 [0.1250; 0.1260] 0.0 1.5 Australia and New Zealand
## NORWAY 3.3743 [3.3717; 3.3769] 0.4 1.5 Northern Europe
## PAKISTAN 0.3093 [0.3092; 0.3094] 1.3 1.5 Southern Asia
## PERU 0.1504 [0.1501; 0.1506] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1064 [0.1063; 0.1065] 0.2 1.5 South-eastern Asia
## POLAND 0.7340 [0.7336; 0.7345] 0.6 1.5 Eastern Europe
## PORTUGAL 3.5756 [3.5737; 3.5776] 0.7 1.5 Southern Europe
## PUERTO RICO 1.1721 [1.1701; 1.1741] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.2316 [0.2312; 0.2319] 0.1 1.5 Eastern Europe
## RUSSIA 0.2277 [0.2276; 0.2278] 0.7 1.5 Eastern Europe
## SAUDI ARABIA 1.3068 [1.3061; 1.3074] 0.9 1.5 Western Asia
## SERBIA 1.3730 [1.3717; 1.3743] 0.2 1.5 Southern Europe
## SLOVAKIA 3.1730 [3.1705; 3.1755] 0.4 1.5 Eastern Europe
## SLOVENIA 3.4915 [3.4873; 3.4957] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2695 [0.2693; 0.2697] 0.3 1.5 Southern Africa
## SOUTH KOREA 1.8268 [1.8262; 1.8274] 1.9 1.5 Eastern Asia
## SPAIN 4.9381 [4.9371; 4.9392] 4.7 1.5 Southern Europe
## SWEDEN 3.2632 [3.2613; 3.2650] 0.7 1.5 Northern Europe
## SWITZERLAND 3.5588 [3.5567; 3.5609] 0.6 1.5 Western Europe
## TAIWAN 0.3043 [0.3040; 0.3047] 0.1 1.5 Eastern Asia
## THAILAND 0.1857 [0.1856; 0.1859] 0.3 1.5 South-eastern Asia
## TUNISIA 0.7481 [0.7473; 0.7489] 0.2 1.5 Northern Africa
## TÜRKIYE 3.2525 [3.2518; 3.2531] 5.4 1.5 Western Asia
## UNITED ARAB EMIRATES 0.6187 [0.6179; 0.6196] 0.1 1.5 Western Asia
## UNITED KINGDOM 7.4479 [7.4468; 7.4490] 10.1 1.5 Northern Europe
## UNITED STATES 2.4957 [2.4955; 2.4960] 16.5 1.5 Northern America
## URUGUAY 1.3737 [1.3716; 1.3757] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.3986 [0.3983; 0.3990] 0.2 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.7818 [2.7817; 2.7819] 43365.34 0
## Random effects model 0.9485 [0.7393; 1.2170] -0.42 0.6778
##
## Quantifying heterogeneity:
## tau^2 = 1.0514 [1.1046; 2.7869]; tau = 1.0254 [1.0510; 1.6694]
## I^2 = 100.0%; H = 5244.10
##
## Test of heterogeneity:
## Q d.f. p-value
## 1760038568.55 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 3.0144 [3.0141; 3.0147] 49308163.85 100.0%
## Region = Central and South America and t ... 10 0.5161 [0.5160; 0.5162] 31632135.56 100.0%
## Region = Northern Europe 8 6.8287 [6.8278; 6.8296] 20395106.50 100.0%
## Region = Eastern Europe 8 1.0264 [1.0261; 1.0267] 63551486.05 100.0%
## Region = Southern Europe 8 3.5018 [3.5013; 3.5023] 27034164.31 100.0%
## Region = Western Europe 7 3.9500 [3.9496; 3.9505] 3221040.26 100.0%
## Region = Australia and New Zealand 2 8.5409 [8.5390; 8.5428] 3849634.22 100.0%
## Region = Eastern Asia 4 2.9184 [2.9180; 2.9188] 157821770.32 100.0%
## Region = Central Asia 1 0.0662 [0.0660; 0.0664] 0.00 --
## Region = South-eastern Asia 2 0.1433 [0.1432; 0.1434] 678844.43 100.0%
## Region = Southern Asia 2 0.1360 [0.1359; 0.1360] 24732456.16 100.0%
## Region = Western Asia 6 2.6164 [2.6160; 2.6169] 22698345.03 100.0%
## Region = Northern Africa 4 2.1124 [2.1120; 2.1128] 21165823.45 100.0%
## Region = Southern Africa 1 0.2695 [0.2693; 0.2697] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1333949598.40 13 0
## Within groups 426088970.15 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.8774 [1.6350; 9.1955] 0.3882 0.6231
## Region = Central and South America and t ... 10 0.4941 [0.3205; 0.7618] 0.4879 0.6985
## Region = Northern Europe 8 2.3780 [1.6776; 3.3707] 0.2535 0.5035
## Region = Eastern Europe 8 0.5921 [0.2485; 1.4107] 1.5695 1.2528
## Region = Southern Europe 8 1.6380 [1.1624; 2.3083] 0.2450 0.4950
## Region = Western Europe 7 3.7764 [3.4289; 4.1591] 0.0170 0.1303
## Region = Australia and New Zealand 2 1.0414 [0.0165; 65.8828] 8.9549 2.9925
## Region = Eastern Asia 4 0.3413 [0.0760; 1.5326] 2.3487 1.5325
## Region = Central Asia 1 0.0662 [0.0660; 0.0664] -- --
## Region = South-eastern Asia 2 0.1406 [0.0814; 0.2427] 0.1554 0.3942
## Region = Southern Asia 2 0.1630 [0.0464; 0.5721] 0.8210 0.9061
## Region = Western Asia 6 1.2843 [0.7279; 2.2658] 0.5034 0.7095
## Region = Northern Africa 4 0.8217 [0.4591; 1.4705] 0.3527 0.5939
## Region = Southern Africa 1 0.2695 [0.2693; 0.2697] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 802928.64 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 2
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 2.8536 [2.8528; 2.8544] 2.2 1.5 Northern Africa
## ARGENTINA 1.2643 [1.2638; 1.2649] 1.0 1.5 Central and South America and the Caribbean
## AUSTRALIA 8.7381 [8.7362; 8.7400] 4.0 1.5 Australia and New Zealand
## AUSTRIA 3.9874 [3.9852; 3.9895] 0.7 1.5 Western Europe
## BELARUS 0.0664 [0.0661; 0.0666] 0.0 1.5 Eastern Europe
## BELGIUM 4.7493 [4.7472; 4.7514] 1.0 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1682 [0.1675; 0.1690] 0.0 1.5 Southern Europe
## BRAZIL 0.5116 [0.5115; 0.5118] 2.0 1.5 Central and South America and the Caribbean
## BULGARIA 0.9454 [0.9442; 0.9466] 0.1 1.5 Eastern Europe
## CANADA 6.0709 [6.0696; 6.0722] 4.2 1.5 Northern America
## CHILE 0.9066 [0.9059; 0.9073] 0.3 1.5 Central and South America and the Caribbean
## CHINA 0.0108 [0.0108; 0.0108] 0.3 1.5 Eastern Asia
## COLOMBIA 0.1810 [0.1808; 0.1812] 0.2 1.5 Central and South America and the Caribbean
## CROATIA 1.2599 [1.2581; 1.2616] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 3.9558 [3.9538; 3.9577] 0.8 1.5 Eastern Europe
## ECUADOR 0.4963 [0.4958; 0.4969] 0.2 1.5 Central and South America and the Caribbean
## EGYPT 3.7697 [3.7691; 3.7704] 6.9 1.5 Northern Africa
## ESTONIA 1.7170 [1.7133; 1.7207] 0.0 1.5 Northern Europe
## FINLAND 5.1854 [5.1823; 5.1886] 0.5 1.5 Northern Europe
## FRANCE 4.6663 [4.6654; 4.6671] 5.6 1.5 Western Europe
## GERMANY 3.9886 [3.9879; 3.9893] 6.1 1.5 Western Europe
## GREECE 3.6452 [3.6433; 3.6471] 0.7 1.5 Southern Europe
## HUNGARY 1.3400 [1.3388; 1.3412] 0.2 1.5 Eastern Europe
## INDIA 0.0924 [0.0923; 0.0924] 2.3 1.5 Southern Asia
## IRELAND 7.2645 [7.2606; 7.2685] 0.6 1.5 Northern Europe
## ITALY 2.3084 [2.3077; 2.3090] 2.6 1.5 Southern Europe
## JAPAN 3.9854 [3.9848; 3.9859] 9.4 1.5 Eastern Asia
## JORDAN 0.3184 [0.3178; 0.3190] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0783 [0.0781; 0.0785] 0.0 1.5 Central Asia
## KUWAIT 2.3085 [2.3061; 2.3110] 0.2 1.5 Western Asia
## LATVIA 0.2861 [0.2849; 0.2874] 0.0 1.5 Northern Europe
## LEBANON 1.0700 [1.0688; 1.0713] 0.1 1.5 Western Asia
## LITHUANIA 1.4162 [1.4139; 1.4185] 0.1 1.5 Northern Europe
## LUXEMBOURG 3.7805 [3.7724; 3.7886] 0.0 1.5 Western Europe
## MEXICO 0.3123 [0.3121; 0.3124] 0.7 1.5 Central and South America and the Caribbean
## MOROCCO 0.1609 [0.1607; 0.1611] 0.1 1.5 Northern Africa
## NETHERLANDS 3.2409 [3.2395; 3.2423] 1.0 1.5 Western Europe
## NEW ZEALAND 1.0352 [1.0336; 1.0367] 0.1 1.5 Australia and New Zealand
## NORWAY 3.4372 [3.4346; 3.4398] 0.3 1.5 Northern Europe
## PAKISTAN 0.3657 [0.3656; 0.3658] 1.4 1.5 Southern Asia
## PERU 0.1859 [0.1856; 0.1861] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1273 [0.1272; 0.1274] 0.3 1.5 South-eastern Asia
## POLAND 1.1714 [1.1709; 1.1720] 0.8 1.5 Eastern Europe
## PORTUGAL 3.6716 [3.6697; 3.6735] 0.7 1.5 Southern Europe
## PUERTO RICO 1.1985 [1.1965; 1.2006] 0.1 1.5 Central and South America and the Caribbean
## ROMANIA 0.2448 [0.2444; 0.2451] 0.1 1.5 Eastern Europe
## RUSSIA 0.2948 [0.2947; 0.2950] 0.8 1.5 Eastern Europe
## SAUDI ARABIA 0.3547 [0.3544; 0.3550] 0.2 1.5 Western Asia
## SERBIA 1.8492 [1.8478; 1.8507] 0.3 1.5 Southern Europe
## SLOVAKIA 3.4508 [3.4482; 3.4534] 0.3 1.5 Eastern Europe
## SLOVENIA 3.6286 [3.6243; 3.6329] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.3076 [0.3074; 0.3079] 0.3 1.5 Southern Africa
## SOUTH KOREA 2.1687 [2.1681; 2.1694] 2.1 1.5 Eastern Asia
## SPAIN 5.1063 [5.1052; 5.1073] 4.4 1.5 Southern Europe
## SWEDEN 3.2313 [3.2294; 3.2331] 0.6 1.5 Northern Europe
## SWITZERLAND 3.7111 [3.7090; 3.7132] 0.6 1.5 Western Europe
## TAIWAN 0.3825 [0.3821; 0.3829] 0.2 1.5 Eastern Asia
## THAILAND 0.2323 [0.2321; 0.2325] 0.3 1.5 South-eastern Asia
## TUNISIA 1.9419 [1.9406; 1.9433] 0.4 1.5 Northern Africa
## TÜRKIYE 3.5747 [3.5740; 3.5753] 5.5 1.5 Western Asia
## UNITED ARAB EMIRATES 0.6074 [0.6066; 0.6082] 0.1 1.5 Western Asia
## UNITED KINGDOM 7.7862 [7.7851; 7.7873] 9.7 1.5 Northern Europe
## UNITED STATES 2.5196 [2.5193; 2.5199] 15.3 1.5 Northern America
## URUGUAY 1.2352 [1.2333; 1.2372] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.3925 [0.3921; 0.3928] 0.2 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.9372 [2.9371; 2.9374] 47830.30 0
## Random effects model 1.0938 [0.8546; 1.4001] 0.71 0.4763
##
## Quantifying heterogeneity:
## tau^2 = 1.0308 [1.0661; 2.6777]; tau = 1.0153 [1.0325; 1.6364]
## I^2 = 100.0%; H = 5446.99
##
## Test of heterogeneity:
## Q d.f. p-value
## 1898862125.23 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 3.0427 [3.0424; 3.0430] 49938160.12 100.0%
## Region = Central and South America and t ... 10 0.5719 [0.5718; 0.5721] 29354395.61 100.0%
## Region = Northern Europe 8 6.9902 [6.9893; 6.9911] 20943407.81 100.0%
## Region = Eastern Europe 8 1.1949 [1.1946; 1.1952] 67049415.72 100.0%
## Region = Southern Europe 8 3.6326 [3.6321; 3.6332] 27391320.40 100.0%
## Region = Western Europe 7 4.2061 [4.2056; 4.2065] 3429166.60 100.0%
## Region = Australia and New Zealand 2 8.3363 [8.3345; 8.3381] 7980102.50 100.0%
## Region = Eastern Asia 4 3.0115 [3.0111; 3.0119] 211671394.36 100.0%
## Region = Central Asia 1 0.0783 [0.0781; 0.0785] 0.00 --
## Region = South-eastern Asia 2 0.1765 [0.1764; 0.1766] 974397.17 100.0%
## Region = Southern Asia 2 0.1565 [0.1565; 0.1565] 33111309.31 100.0%
## Region = Western Asia 6 2.9958 [2.9952; 2.9963] 37576465.50 100.0%
## Region = Northern Africa 4 3.3155 [3.3151; 3.3160] 24954146.52 100.0%
## Region = Southern Africa 1 0.3076 [0.3074; 0.3079] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1384488443.59 13 0
## Within groups 514373681.64 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 3.9110 [1.6520; 9.2590] 0.3867 0.6218
## Region = Central and South America and t ... 10 0.5308 [0.3577; 0.7877] 0.4056 0.6368
## Region = Northern Europe 8 2.6267 [1.8349; 3.7603] 0.2681 0.5177
## Region = Eastern Europe 8 0.7471 [0.3357; 1.6624] 1.3323 1.1542
## Region = Southern Europe 8 1.9673 [1.4062; 2.7523] 0.2348 0.4846
## Region = Western Europe 7 3.9874 [3.6214; 4.3904] 0.0169 0.1300
## Region = Australia and New Zealand 2 3.0075 [0.3718; 24.3265] 2.2751 1.5084
## Region = Eastern Asia 4 0.4348 [0.0895; 2.1125] 2.6019 1.6130
## Region = Central Asia 1 0.0783 [0.0781; 0.0785] -- --
## Region = South-eastern Asia 2 0.1720 [0.0954; 0.3101] 0.1809 0.4254
## Region = Southern Asia 2 0.1838 [0.0477; 0.7079] 0.9468 0.9731
## Region = Western Asia 6 0.9198 [0.3497; 2.4196] 1.4609 1.2087
## Region = Northern Africa 4 1.3540 [0.7899; 2.3208] 0.3023 0.5499
## Region = Southern Africa 1 0.3076 [0.3074; 0.3079] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 910810.12 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.0709 [0.0708; 0.0711] 0.1 1.7 Northern Africa
## ARGENTINA 0.2110 [0.2108; 0.2113] 0.2 1.7 Central and South America and the Caribbean
## AUSTRALIA 1.2865 [1.2857; 1.2873] 0.7 1.7 Australia and New Zealand
## AUSTRIA 2.1978 [2.1961; 2.1995] 0.5 1.7 Western Europe
## BELARUS 0.0056 [0.0055; 0.0057] 0.0 1.7 Eastern Europe
## BELGIUM 1.3012 [1.3001; 1.3024] 0.4 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0522 [0.0522; 0.0523] 0.3 1.7 Central and South America and the Caribbean
## BULGARIA 0.1053 [0.1049; 0.1057] 0.0 1.7 Eastern Europe
## CANADA 4.1266 [4.1254; 4.1277] 3.7 1.7 Northern America
## CHILE 0.2113 [0.2110; 0.2117] 0.1 1.7 Central and South America and the Caribbean
## CHINA 0.0009 [0.0009; 0.0009] 0.0 1.7 Eastern Asia
## COLOMBIA 0.0876 [0.0874; 0.0877] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.3160 [0.3151; 0.3168] 0.0 1.7 Southern Europe
## CZECH REPUBLIC 1.1416 [1.1405; 1.1427] 0.3 1.7 Eastern Europe
## ECUADOR 0.1948 [0.1944; 0.1951] 0.1 1.7 Central and South America and the Caribbean
## EGYPT 0.1064 [0.1063; 0.1066] 0.2 1.7 Northern Africa
## ESTONIA 0.2020 [0.2007; 0.2032] 0.0 1.7 Northern Europe
## FINLAND 4.3558 [4.3528; 4.3587] 0.6 1.7 Northern Europe
## FRANCE 3.5466 [3.5458; 3.5473] 5.9 1.7 Western Europe
## GERMANY 2.7848 [2.7842; 2.7854] 6.1 1.7 Western Europe
## GREECE 2.2484 [2.2470; 2.2499] 0.7 1.7 Southern Europe
## HUNGARY 0.7213 [0.7204; 0.7221] 0.2 1.7 Eastern Europe
## INDIA 0.0537 [0.0537; 0.0537] 1.7 1.7 Southern Asia
## IRELAND 2.8727 [2.8700; 2.8753] 0.3 1.7 Northern Europe
## ITALY 1.4918 [1.4913; 1.4923] 2.4 1.7 Southern Europe
## JAPAN 0.1016 [0.1015; 0.1017] 0.4 1.7 Eastern Asia
## JORDAN 0.1265 [0.1261; 0.1270] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1175 [0.1168; 0.1182] 0.0 1.7 Western Asia
## LATVIA 0.4164 [0.4150; 0.4178] 0.0 1.7 Northern Europe
## LEBANON 0.4021 [0.4012; 0.4031] 0.1 1.7 Western Asia
## LITHUANIA 0.3469 [0.3459; 0.3480] 0.0 1.7 Northern Europe
## LUXEMBOURG 3.2514 [3.2430; 3.2598] 0.0 1.7 Western Europe
## MEXICO 0.2788 [0.2786; 0.2789] 0.8 1.7 Central and South America and the Caribbean
## MOROCCO 0.0236 [0.0235; 0.0237] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 0.9869 [0.9853; 0.9885] 0.1 1.7 Australia and New Zealand
## NORWAY 3.4299 [3.4272; 3.4327] 0.4 1.7 Northern Europe
## PAKISTAN 0.0973 [0.0972; 0.0974] 0.4 1.7 Southern Asia
## PERU 0.0497 [0.0495; 0.0498] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0530 [0.0529; 0.0531] 0.1 1.7 South-eastern Asia
## POLAND 0.1513 [0.1511; 0.1515] 0.2 1.7 Eastern Europe
## PORTUGAL 3.1225 [3.1207; 3.1243] 0.9 1.7 Southern Europe
## PUERTO RICO 5.0091 [5.0053; 5.0129] 0.5 1.7 Central and South America and the Caribbean
## ROMANIA 0.3109 [0.3105; 0.3113] 0.2 1.7 Eastern Europe
## RUSSIA 0.0289 [0.0289; 0.0290] 0.1 1.7 Eastern Europe
## SAUDI ARABIA 0.3616 [0.3612; 0.3620] 0.3 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 1.4112 [1.4096; 1.4129] 0.2 1.7 Eastern Europe
## SLOVENIA 1.5309 [1.5280; 1.5337] 0.1 1.7 Southern Europe
## SOUTH AFRICA 0.1159 [0.1158; 0.1161] 0.2 1.7 Southern Africa
## SOUTH KOREA 0.9479 [0.9475; 0.9484] 1.3 1.7 Eastern Asia
## SPAIN 3.8350 [3.8340; 3.8359] 4.8 1.7 Southern Europe
## SWEDEN 3.3977 [3.3958; 3.3997] 0.8 1.7 Northern Europe
## SWITZERLAND 1.9623 [1.9607; 1.9640] 0.4 1.7 Western Europe
## TAIWAN 0.1750 [0.1747; 0.1753] 0.1 1.7 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.1223 [0.1220; 0.1227] 0.0 1.7 Northern Africa
## TÜRKIYE 1.3869 [1.3864; 1.3873] 2.6 1.7 Western Asia
## UNITED ARAB EMIRATES 0.3101 [0.3094; 0.3108] 0.1 1.7 Western Asia
## UNITED KINGDOM 3.0456 [3.0449; 3.0463] 5.1 1.7 Northern Europe
## UNITED STATES 6.6631 [6.6626; 6.6636] 54.5 1.7 Northern America
## URUGUAY 0.2539 [0.2530; 0.2548] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.6793 [0.6788; 0.6798] 0.5 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.6284 [3.6282; 3.6286] 47464.74 0
## Random effects model 0.4114 [0.2989; 0.5663] -5.45 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.5947 [1.5270; 4.6989]; tau = 1.2628 [1.2357; 2.1677]
## I^2 = 100.0%; H = 5021.10
##
## Test of heterogeneity:
## Q d.f. p-value
## 1487474580.99 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 6.4628 [6.4623; 6.4632] 10800513.39 100.0%
## Region = Central and South America and t ... 10 0.4213 [0.4211; 0.4214] 68560818.30 100.0%
## Region = Northern Europe 8 3.1351 [3.1345; 3.1357] 5193026.38 100.0%
## Region = Eastern Europe 8 0.4678 [0.4676; 0.4681] 23190551.74 100.0%
## Region = Southern Europe 6 2.7450 [2.7445; 2.7455] 22449216.66 100.0%
## Region = Western Europe 6 2.9787 [2.9783; 2.9792] 7916037.00 100.0%
## Region = Australia and New Zealand 2 1.2418 [1.2411; 1.2425] 93922.42 100.0%
## Region = Eastern Asia 4 0.4761 [0.4759; 0.4763] 38921435.09 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0530 [0.0529; 0.0531] 0.00 --
## Region = Southern Asia 2 0.0607 [0.0607; 0.0607] 1714103.30 100.0%
## Region = Western Asia 6 1.1502 [1.1499; 1.1506] 10049205.60 100.0%
## Region = Northern Africa 4 0.0916 [0.0915; 0.0917] 667802.23 100.0%
## Region = Southern Africa 1 0.1159 [0.1158; 0.1161] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1297917948.88 12 0
## Within groups 189556632.11 47 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 5.2436 [3.2788; 8.3859] 0.1148 0.3388
## Region = Central and South America and t ... 10 0.2332 [0.0903; 0.6024] 2.3447 1.5312
## Region = Northern Europe 8 1.3775 [1.1026; 1.7208] 0.1031 0.3211
## Region = Eastern Europe 8 0.1763 [0.0704; 0.4416] 1.7561 1.3252
## Region = Southern Europe 6 1.6396 [1.0552; 2.5477] 0.3034 0.5508
## Region = Western Europe 6 2.3766 [1.9115; 2.9549] 0.0741 0.2722
## Region = Australia and New Zealand 2 1.1268 [0.8690; 1.4610] 0.0351 0.1874
## Region = Eastern Asia 4 0.0630 [0.0095; 0.4177] 3.7229 1.9295
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0530 [0.0529; 0.0531] -- --
## Region = Southern Asia 2 0.0723 [0.0404; 0.1295] 0.1769 0.4206
## Region = Western Asia 6 0.3124 [0.1382; 0.7063] 1.0395 1.0196
## Region = Northern Africa 4 0.0683 [0.0411; 0.1137] 0.2698 0.5194
## Region = Southern Africa 1 0.1159 [0.1158; 0.1161] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 589746.31 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.2753 [0.2750; 0.2756] 0.2 1.7 Northern Africa
## ARGENTINA 0.2916 [0.2913; 0.2918] 0.3 1.7 Central and South America and the Caribbean
## AUSTRALIA 1.4792 [1.4784; 1.4801] 0.8 1.7 Australia and New Zealand
## AUSTRIA 2.5392 [2.5375; 2.5410] 0.5 1.7 Western Europe
## BELARUS 0.0141 [0.0139; 0.0142] 0.0 1.7 Eastern Europe
## BELGIUM 1.5782 [1.5770; 1.5795] 0.4 1.7 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.0739 [0.0739; 0.0740] 0.3 1.7 Central and South America and the Caribbean
## BULGARIA 0.2261 [0.2255; 0.2267] 0.0 1.7 Eastern Europe
## CANADA 4.7887 [4.7875; 4.7899] 3.8 1.7 Northern America
## CHILE 0.2263 [0.2259; 0.2266] 0.1 1.7 Central and South America and the Caribbean
## CHINA 0.0020 [0.0020; 0.0020] 0.1 1.7 Eastern Asia
## COLOMBIA 0.0840 [0.0839; 0.0841] 0.1 1.7 Central and South America and the Caribbean
## CROATIA 0.3611 [0.3602; 0.3621] 0.0 1.7 Southern Europe
## CZECH REPUBLIC 1.4811 [1.4799; 1.4823] 0.4 1.7 Eastern Europe
## ECUADOR 0.2420 [0.2416; 0.2424] 0.1 1.7 Central and South America and the Caribbean
## EGYPT 0.1499 [0.1497; 0.1500] 0.3 1.7 Northern Africa
## ESTONIA 0.2543 [0.2529; 0.2557] 0.0 1.7 Northern Europe
## FINLAND 4.9666 [4.9635; 4.9698] 0.6 1.7 Northern Europe
## FRANCE 3.8551 [3.8543; 3.8559] 5.6 1.7 Western Europe
## GERMANY 3.2166 [3.2159; 3.2172] 6.1 1.7 Western Europe
## GREECE 2.6236 [2.6221; 2.6252] 0.7 1.7 Southern Europe
## HUNGARY 0.8353 [0.8344; 0.8363] 0.2 1.7 Eastern Europe
## INDIA 0.0629 [0.0629; 0.0630] 1.8 1.7 Southern Asia
## IRELAND 3.5075 [3.5046; 3.5103] 0.4 1.7 Northern Europe
## ITALY 1.6554 [1.6548; 1.6559] 2.3 1.7 Southern Europe
## JAPAN 0.1478 [0.1477; 0.1479] 0.4 1.7 Eastern Asia
## JORDAN 0.1551 [0.1546; 0.1556] 0.0 1.7 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1514 [0.1507; 0.1522] 0.0 1.7 Western Asia
## LATVIA 0.5005 [0.4989; 0.5021] 0.0 1.7 Northern Europe
## LEBANON 0.5855 [0.5844; 0.5867] 0.1 1.7 Western Asia
## LITHUANIA 0.4040 [0.4028; 0.4051] 0.0 1.7 Northern Europe
## LUXEMBOURG 3.3859 [3.3774; 3.3943] 0.0 1.7 Western Europe
## MEXICO 0.2677 [0.2676; 0.2679] 0.7 1.7 Central and South America and the Caribbean
## MOROCCO 0.0386 [0.0385; 0.0387] 0.0 1.7 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 1.1928 [1.1911; 1.1945] 0.1 1.7 Australia and New Zealand
## NORWAY 3.6102 [3.6074; 3.6130] 0.4 1.7 Northern Europe
## PAKISTAN 0.1376 [0.1375; 0.1377] 0.6 1.7 Southern Asia
## PERU 0.0548 [0.0547; 0.0549] 0.0 1.7 Central and South America and the Caribbean
## PHILIPPINES 0.0637 [0.0636; 0.0638] 0.1 1.7 South-eastern Asia
## POLAND 0.1745 [0.1743; 0.1747] 0.2 1.7 Eastern Europe
## PORTUGAL 3.3821 [3.3803; 3.3840] 0.8 1.7 Southern Europe
## PUERTO RICO 5.9562 [5.9520; 5.9603] 0.5 1.7 Central and South America and the Caribbean
## ROMANIA 0.5838 [0.5832; 0.5843] 0.3 1.7 Eastern Europe
## RUSSIA 0.0450 [0.0449; 0.0451] 0.2 1.7 Eastern Europe
## SAUDI ARABIA 0.4092 [0.4088; 0.4096] 0.3 1.7 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 1.8496 [1.8477; 1.8515] 0.2 1.7 Eastern Europe
## SLOVENIA 1.7773 [1.7743; 1.7803] 0.1 1.7 Southern Europe
## SOUTH AFRICA 0.1586 [0.1584; 0.1588] 0.2 1.7 Southern Africa
## SOUTH KOREA 1.1294 [1.1289; 1.1299] 1.3 1.7 Eastern Asia
## SPAIN 4.4325 [4.4315; 4.4335] 4.8 1.7 Southern Europe
## SWEDEN 3.9503 [3.9482; 3.9524] 0.9 1.7 Northern Europe
## SWITZERLAND 2.1416 [2.1399; 2.1433] 0.4 1.7 Western Europe
## TAIWAN 0.1779 [0.1776; 0.1781] 0.1 1.7 Eastern Asia
## THAILAND 0.0000 0.0 0.0 South-eastern Asia
## TUNISIA 0.2006 [0.2002; 0.2011] 0.0 1.7 Northern Africa
## TÜRKIYE 1.7331 [1.7326; 1.7337] 2.9 1.7 Western Asia
## UNITED ARAB EMIRATES 0.4496 [0.4488; 0.4503] 0.1 1.7 Western Asia
## UNITED KINGDOM 3.8182 [3.8174; 3.8190] 5.6 1.7 Northern Europe
## UNITED STATES 7.4008 [7.4003; 7.4013] 53.0 1.7 Northern America
## URUGUAY 0.4441 [0.4430; 0.4453] 0.0 1.7 Central and South America and the Caribbean
## VENEZUELA 0.7331 [0.7326; 0.7337] 0.5 1.7 Central and South America and the Caribbean
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.9825 [3.9823; 3.9827] 54600.50 0
## Random effects model 0.5307 [0.3854; 0.7308] -3.88 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.5991 [1.4772; 4.4476]; tau = 1.2646 [1.2154; 2.1089]
## I^2 = 100.0%; H = 5451.73
##
## Test of heterogeneity:
## Q d.f. p-value
## 1753562347.73 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 7.1895 [7.1890; 7.1900] 10441148.67 100.0%
## Region = Central and South America and t ... 10 0.4672 [0.4671; 0.4674] 82371401.59 100.0%
## Region = Northern Europe 8 3.8175 [3.8168; 3.8182] 5634592.31 100.0%
## Region = Eastern Europe 8 0.6058 [0.6056; 0.6061] 30395566.61 100.0%
## Region = Southern Europe 6 3.1450 [3.1444; 3.1455] 27126580.72 100.0%
## Region = Western Europe 6 3.3328 [3.3323; 3.3332] 7239094.92 100.0%
## Region = Australia and New Zealand 2 1.4359 [1.4351; 1.4367] 75178.21 100.0%
## Region = Eastern Asia 4 0.5196 [0.5194; 0.5198] 55464598.05 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0637 [0.0636; 0.0638] 0.00 --
## Region = Southern Asia 2 0.0759 [0.0759; 0.0759] 4101885.53 100.0%
## Region = Western Asia 6 1.4316 [1.4312; 1.4320] 13175229.32 100.0%
## Region = Northern Africa 4 0.1817 [0.1816; 0.1818] 1865787.36 100.0%
## Region = Southern Africa 1 0.1586 [0.1584; 0.1588] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1515671284.44 12 0
## Within groups 237891063.28 47 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 5.9532 [3.8858; 9.1206] 0.0948 0.3078
## Region = Central and South America and t ... 10 0.2786 [0.1062; 0.7312] 2.4234 1.5567
## Region = Northern Europe 8 1.6257 [1.3124; 2.0137] 0.0954 0.3089
## Region = Eastern Europe 8 0.2757 [0.1130; 0.6724] 1.6557 1.2867
## Region = Southern Europe 6 1.8629 [1.1810; 2.9385] 0.3245 0.5696
## Region = Western Europe 6 2.6676 [2.1929; 3.2450] 0.0600 0.2449
## Region = Australia and New Zealand 2 1.3283 [1.0758; 1.6402] 0.0232 0.1522
## Region = Eastern Asia 4 0.0875 [0.0126; 0.6096] 3.9228 1.9806
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 1 0.0637 [0.0636; 0.0638] -- --
## Region = Southern Asia 2 0.0930 [0.0432; 0.2002] 0.3058 0.5530
## Region = Western Asia 6 0.4046 [0.1776; 0.9215] 1.0585 1.0288
## Region = Northern Africa 4 0.1337 [0.0760; 0.2351] 0.3321 0.5763
## Region = Southern Africa 1 0.1586 [0.1584; 0.1588] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1033311.92 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.3584 [0.3581; 0.3587] 0.3 1.6 Northern Africa
## ARGENTINA 0.4124 [0.4120; 0.4127] 0.3 1.6 Central and South America and the Caribbean
## AUSTRALIA 1.6897 [1.6888; 1.6906] 0.7 1.6 Australia and New Zealand
## AUSTRIA 3.0211 [3.0192; 3.0231] 0.5 1.6 Western Europe
## BELARUS 0.0180 [0.0178; 0.0181] 0.0 1.6 Eastern Europe
## BELGIUM 2.2085 [2.2071; 2.2100] 0.5 1.6 Western Europe
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 Southern Europe
## BRAZIL 0.1212 [0.1211; 0.1213] 0.5 1.6 Central and South America and the Caribbean
## BULGARIA 0.3263 [0.3256; 0.3270] 0.0 1.6 Eastern Europe
## CANADA 5.3477 [5.3464; 5.3490] 3.7 1.6 Northern America
## CHILE 0.2580 [0.2576; 0.2584] 0.1 1.6 Central and South America and the Caribbean
## CHINA 0.0031 [0.0031; 0.0031] 0.1 1.6 Eastern Asia
## COLOMBIA 0.0760 [0.0759; 0.0762] 0.1 1.6 Central and South America and the Caribbean
## CROATIA 0.4543 [0.4532; 0.4553] 0.0 1.6 Southern Europe
## CZECH REPUBLIC 1.8185 [1.8172; 1.8199] 0.4 1.6 Eastern Europe
## ECUADOR 0.2917 [0.2912; 0.2921] 0.1 1.6 Central and South America and the Caribbean
## EGYPT 0.2397 [0.2396; 0.2399] 0.4 1.6 Northern Africa
## ESTONIA 0.4373 [0.4355; 0.4392] 0.0 1.6 Northern Europe
## FINLAND 5.4352 [5.4319; 5.4384] 0.6 1.6 Northern Europe
## FRANCE 4.1757 [4.1748; 4.1765] 5.3 1.6 Western Europe
## GERMANY 3.5994 [3.5987; 3.6001] 5.8 1.6 Western Europe
## GREECE 2.8252 [2.8236; 2.8269] 0.6 1.6 Southern Europe
## HUNGARY 1.0156 [1.0146; 1.0167] 0.2 1.6 Eastern Europe
## INDIA 0.0720 [0.0719; 0.0720] 1.8 1.6 Southern Asia
## IRELAND 3.9361 [3.9331; 3.9391] 0.4 1.6 Northern Europe
## ITALY 1.8485 [1.8479; 1.8491] 2.2 1.6 Southern Europe
## JAPAN 0.3150 [0.3149; 0.3152] 0.8 1.6 Eastern Asia
## JORDAN 0.1833 [0.1828; 0.1838] 0.0 1.6 Western Asia
## KAZAKHSTAN 0.0000 0.0 0.0 Central Asia
## KUWAIT 0.1562 [0.1555; 0.1569] 0.0 1.6 Western Asia
## LATVIA 0.5646 [0.5630; 0.5663] 0.0 1.6 Northern Europe
## LEBANON 0.6460 [0.6448; 0.6471] 0.1 1.6 Western Asia
## LITHUANIA 0.5072 [0.5059; 0.5085] 0.0 1.6 Northern Europe
## LUXEMBOURG 3.6404 [3.6317; 3.6491] 0.0 1.6 Western Europe
## MEXICO 0.2720 [0.2718; 0.2721] 0.6 1.6 Central and South America and the Caribbean
## MOROCCO 0.0432 [0.0431; 0.0433] 0.0 1.6 Northern Africa
## NETHERLANDS 0.0000 0.0 0.0 Western Europe
## NEW ZEALAND 1.4364 [1.4345; 1.4383] 0.1 1.6 Australia and New Zealand
## NORWAY 3.9539 [3.9510; 3.9568] 0.4 1.6 Northern Europe
## PAKISTAN 0.1573 [0.1572; 0.1574] 0.6 1.6 Southern Asia
## PERU 0.0700 [0.0698; 0.0702] 0.0 1.6 Central and South America and the Caribbean
## PHILIPPINES 0.0686 [0.0685; 0.0687] 0.1 1.6 South-eastern Asia
## POLAND 0.2009 [0.2007; 0.2012] 0.2 1.6 Eastern Europe
## PORTUGAL 3.8120 [3.8101; 3.8140] 0.8 1.6 Southern Europe
## PUERTO RICO 6.7601 [6.7556; 6.7645] 0.5 1.6 Central and South America and the Caribbean
## ROMANIA 0.5874 [0.5868; 0.5879] 0.2 1.6 Eastern Europe
## RUSSIA 0.0884 [0.0883; 0.0885] 0.3 1.6 Eastern Europe
## SAUDI ARABIA 0.4869 [0.4865; 0.4873] 0.3 1.6 Western Asia
## SERBIA 0.0000 0.0 0.0 Southern Europe
## SLOVAKIA 2.3771 [2.3750; 2.3793] 0.3 1.6 Eastern Europe
## SLOVENIA 2.0489 [2.0457; 2.0522] 0.1 1.6 Southern Europe
## SOUTH AFRICA 0.1825 [0.1823; 0.1827] 0.2 1.6 Southern Africa
## SOUTH KOREA 1.2561 [1.2556; 1.2566] 1.2 1.6 Eastern Asia
## SPAIN 4.8978 [4.8967; 4.8988] 4.6 1.6 Southern Europe
## SWEDEN 4.3114 [4.3092; 4.3136] 0.8 1.6 Northern Europe
## SWITZERLAND 2.4739 [2.4721; 2.4758] 0.4 1.6 Western Europe
## TAIWAN 0.1970 [0.1967; 0.1973] 0.1 1.6 Eastern Asia
## THAILAND 0.2391 [0.2389; 0.2393] 0.3 1.6 South-eastern Asia
## TUNISIA 0.2414 [0.2409; 0.2418] 0.1 1.6 Northern Africa
## TÜRKIYE 2.1626 [2.1620; 2.1632] 3.1 1.6 Western Asia
## UNITED ARAB EMIRATES 0.7453 [0.7443; 0.7462] 0.1 1.6 Western Asia
## UNITED KINGDOM 4.7452 [4.7443; 4.7461] 6.0 1.6 Northern Europe
## UNITED STATES 8.5063 [8.5057; 8.5068] 52.6 1.6 Northern America
## URUGUAY 0.5483 [0.5470; 0.5496] 0.0 1.6 Central and South America and the Caribbean
## VENEZUELA 0.7677 [0.7672; 0.7682] 0.4 1.6 Central and South America and the Caribbean
##
## Number of studies combined: k = 61
##
## rate 95%-CI z p-value
## Common effect model 4.4356 [4.4354; 4.4358] 63637.75 0
## Random effects model 0.6343 [0.4586; 0.8775] -2.75 0.0060
##
## Quantifying heterogeneity:
## tau^2 = 1.6714 [1.4492; 4.2686]; tau = 1.2928 [1.2038; 2.0660]
## I^2 = 100.0%; H = 5996.98
##
## Test of heterogeneity:
## Q d.f. p-value
## 2157825551.28 60 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 8.2536 [8.2531; 8.2541] 13435200.90 100.0%
## Region = Central and South America and t ... 10 0.5039 [0.5038; 0.5041] 91736506.43 100.0%
## Region = Northern Europe 8 4.5834 [4.5827; 4.5841] 6697879.29 100.0%
## Region = Eastern Europe 8 0.6761 [0.6758; 0.6763] 39565190.94 100.0%
## Region = Southern Europe 6 3.4819 [3.4813; 3.4824] 29854484.84 100.0%
## Region = Western Europe 6 3.6900 [3.6896; 3.6905] 5356053.78 100.0%
## Region = Australia and New Zealand 2 1.6508 [1.6499; 1.6516] 51792.47 100.0%
## Region = Eastern Asia 4 0.5602 [0.5601; 0.5604] 63566747.42 100.0%
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.1672 [0.1671; 0.1673] 2618838.90 100.0%
## Region = Southern Asia 2 0.0869 [0.0869; 0.0869] 4781560.53 100.0%
## Region = Western Asia 6 1.7819 [1.7815; 1.7823] 16848115.86 100.0%
## Region = Northern Africa 4 0.2588 [0.2586; 0.2589] 2181537.78 100.0%
## Region = Southern Africa 1 0.1825 [0.1823; 0.1827] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1881131642.14 12 0
## Within groups 276693909.14 48 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 6.7446 [4.2797; 10.6290] 0.1077 0.3282
## Region = Central and South America and t ... 10 0.3304 [0.1305; 0.8364] 2.2459 1.4986
## Region = Northern Europe 8 1.9588 [1.5732; 2.4390] 0.1001 0.3164
## Region = Eastern Europe 8 0.3578 [0.1453; 0.8809] 1.6910 1.3004
## Region = Southern Europe 6 2.1199 [1.3476; 3.3347] 0.3206 0.5662
## Region = Western Europe 6 3.1091 [2.6544; 3.6416] 0.0390 0.1976
## Region = Australia and New Zealand 2 1.5579 [1.3287; 1.8267] 0.0132 0.1148
## Region = Eastern Asia 4 0.1249 [0.0241; 0.6467] 2.8160 1.6781
## Region = Central Asia 0 NA -- --
## Region = South-eastern Asia 2 0.1281 [0.0377; 0.4353] 0.7793 0.8828
## Region = Southern Asia 2 0.1064 [0.0495; 0.2289] 0.3056 0.5528
## Region = Western Asia 6 0.4939 [0.2193; 1.1124] 1.0298 1.0148
## Region = Northern Africa 4 0.1730 [0.1029; 0.2911] 0.2816 0.5307
## Region = Southern Africa 1 0.1825 [0.1823; 0.1827] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 2748.98 12 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.5023 [0.5019; 0.5026] 0.3 1.5 Northern Africa
## ARGENTINA 0.6236 [0.6232; 0.6240] 0.5 1.5 Central and South America and the Caribbean
## AUSTRALIA 1.9091 [1.9081; 1.9100] 0.8 1.5 Australia and New Zealand
## AUSTRIA 3.4460 [3.4440; 3.4481] 0.5 1.5 Western Europe
## BELARUS 0.0188 [0.0187; 0.0190] 0.0 1.5 Eastern Europe
## BELGIUM 2.6343 [2.6327; 2.6359] 0.5 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.0490 [0.0486; 0.0494] 0.0 1.5 Southern Europe
## BRAZIL 0.1618 [0.1617; 0.1619] 0.6 1.5 Central and South America and the Caribbean
## BULGARIA 0.4892 [0.4884; 0.4901] 0.1 1.5 Eastern Europe
## CANADA 5.8395 [5.8381; 5.8408] 3.6 1.5 Northern America
## CHILE 0.3141 [0.3137; 0.3146] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0053 [0.0053; 0.0053] 0.1 1.5 Eastern Asia
## COLOMBIA 0.0820 [0.0819; 0.0821] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.4773 [0.4762; 0.4783] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.2432 [2.2417; 2.2447] 0.4 1.5 Eastern Europe
## ECUADOR 0.3322 [0.3317; 0.3327] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.3250 [0.3248; 0.3252] 0.5 1.5 Northern Africa
## ESTONIA 0.5211 [0.5191; 0.5231] 0.0 1.5 Northern Europe
## FINLAND 5.7890 [5.7856; 5.7923] 0.6 1.5 Northern Europe
## FRANCE 4.3160 [4.3151; 4.3168] 4.9 1.5 Western Europe
## GERMANY 3.8865 [3.8858; 3.8872] 5.6 1.5 Western Europe
## GREECE 3.0761 [3.0744; 3.0778] 0.6 1.5 Southern Europe
## HUNGARY 1.2037 [1.2026; 1.2048] 0.2 1.5 Eastern Europe
## INDIA 0.0808 [0.0808; 0.0808] 1.8 1.5 Southern Asia
## IRELAND 4.6899 [4.6866; 4.6932] 0.4 1.5 Northern Europe
## ITALY 2.0384 [2.0378; 2.0390] 2.2 1.5 Southern Europe
## JAPAN 1.0988 [1.0985; 1.0991] 2.5 1.5 Eastern Asia
## JORDAN 0.2052 [0.2047; 0.2057] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0285 [0.0283; 0.0286] 0.0 1.5 Central Asia
## KUWAIT 0.2263 [0.2254; 0.2271] 0.0 1.5 Western Asia
## LATVIA 0.6788 [0.6770; 0.6807] 0.0 1.5 Northern Europe
## LEBANON 0.7612 [0.7600; 0.7625] 0.1 1.5 Western Asia
## LITHUANIA 0.7232 [0.7217; 0.7248] 0.0 1.5 Northern Europe
## LUXEMBOURG 3.7951 [3.7863; 3.8039] 0.0 1.5 Western Europe
## MEXICO 0.2476 [0.2474; 0.2477] 0.5 1.5 Central and South America and the Caribbean
## MOROCCO 0.0533 [0.0531; 0.0534] 0.0 1.5 Northern Africa
## NETHERLANDS 2.5304 [2.5291; 2.5317] 0.8 1.5 Western Europe
## NEW ZEALAND 1.6623 [1.6603; 1.6643] 0.1 1.5 Australia and New Zealand
## NORWAY 4.4844 [4.4813; 4.4874] 0.4 1.5 Northern Europe
## PAKISTAN 0.1948 [0.1947; 0.1949] 0.6 1.5 Southern Asia
## PERU 0.0813 [0.0811; 0.0815] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0735 [0.0734; 0.0736] 0.1 1.5 South-eastern Asia
## POLAND 0.2380 [0.2377; 0.2383] 0.2 1.5 Eastern Europe
## PORTUGAL 3.8753 [3.8734; 3.8773] 0.7 1.5 Southern Europe
## PUERTO RICO 8.4500 [8.4450; 8.4550] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 0.7133 [0.7126; 0.7139] 0.3 1.5 Eastern Europe
## RUSSIA 0.1598 [0.1597; 0.1599] 0.4 1.5 Eastern Europe
## SAUDI ARABIA 0.5929 [0.5924; 0.5933] 0.3 1.5 Western Asia
## SERBIA 0.0947 [0.0944; 0.0950] 0.0 1.5 Southern Europe
## SLOVAKIA 2.9752 [2.9728; 2.9776] 0.3 1.5 Eastern Europe
## SLOVENIA 2.2320 [2.2286; 2.2354] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2093 [0.2091; 0.2095] 0.2 1.5 Southern Africa
## SOUTH KOREA 1.3683 [1.3677; 1.3688] 1.2 1.5 Eastern Asia
## SPAIN 5.4072 [5.4061; 5.4083] 4.6 1.5 Southern Europe
## SWEDEN 4.6491 [4.6469; 4.6514] 0.8 1.5 Northern Europe
## SWITZERLAND 2.7352 [2.7333; 2.7371] 0.4 1.5 Western Europe
## TAIWAN 0.2174 [0.2171; 0.2177] 0.1 1.5 Eastern Asia
## THAILAND 0.3580 [0.3578; 0.3583] 0.4 1.5 South-eastern Asia
## TUNISIA 0.2845 [0.2840; 0.2851] 0.1 1.5 Northern Africa
## TÜRKIYE 2.7223 [2.7216; 2.7229] 3.6 1.5 Western Asia
## UNITED ARAB EMIRATES 0.8151 [0.8141; 0.8160] 0.1 1.5 Western Asia
## UNITED KINGDOM 5.6548 [5.6539; 5.6558] 6.5 1.5 Northern Europe
## UNITED STATES 8.7465 [8.7459; 8.7470] 48.9 1.5 Northern America
## URUGUAY 0.6987 [0.6973; 0.7002] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.8805 [0.8799; 0.8810] 0.5 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.4452 [4.4450; 4.4454] 67329.03 0
## Random effects model 0.6891 [0.5086; 0.9337] -2.40 0.0163
##
## Quantifying heterogeneity:
## tau^2 = 1.5618 [1.3732; 3.8752]; tau = 1.2497 [1.1718; 1.9686]
## I^2 = 100.0%; H = 6082.87
##
## Test of heterogeneity:
## Q d.f. p-value
## 2368087497.44 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 8.5063 [8.5058; 8.5069] 11195894.64 100.0%
## Region = Central and South America and t ... 10 0.6179 [0.6177; 0.6181] 116822894.30 100.0%
## Region = Northern Europe 8 5.3564 [5.3556; 5.3572] 8027178.92 100.0%
## Region = Eastern Europe 8 0.7868 [0.7866; 0.7871] 48010225.50 100.0%
## Region = Southern Europe 8 3.7776 [3.7770; 3.7782] 38499842.30 100.0%
## Region = Western Europe 7 3.8214 [3.8210; 3.8219] 6601696.05 100.0%
## Region = Australia and New Zealand 2 1.8709 [1.8701; 1.8718] 43916.07 100.0%
## Region = Eastern Asia 4 0.9503 [0.9501; 0.9505] 80189240.15 100.0%
## Region = Central Asia 1 0.0285 [0.0283; 0.0286] 0.00 --
## Region = South-eastern Asia 2 0.2507 [0.2505; 0.2508] 4984882.61 100.0%
## Region = Southern Asia 2 0.1017 [0.1017; 0.1017] 7456870.97 100.0%
## Region = Western Asia 6 2.2367 [2.2362; 2.2372] 22656268.47 100.0%
## Region = Northern Africa 4 0.3548 [0.3546; 0.3550] 3237663.07 100.0%
## Region = Southern Africa 1 0.2093 [0.2091; 0.2095] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2020360924.39 13 0
## Within groups 347726573.05 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 7.1467 [4.8101; 10.6182] 0.0816 0.2857
## Region = Central and South America and t ... 10 0.3941 [0.1519; 1.0227] 2.3671 1.5386
## Region = Northern Europe 8 2.3129 [1.8457; 2.8984] 0.1060 0.3256
## Region = Eastern Europe 8 0.4601 [0.1938; 1.0920] 1.5560 1.2474
## Region = Southern Europe 8 0.9475 [0.6185; 1.4517] 0.3790 0.6156
## Region = Western Europe 7 3.2702 [2.8245; 3.7862] 0.0391 0.1978
## Region = Australia and New Zealand 2 1.7814 [1.5554; 2.0402] 0.0096 0.0979
## Region = Eastern Asia 4 0.2040 [0.0512; 0.8124] 1.9877 1.4099
## Region = Central Asia 1 0.0285 [0.0283; 0.0286] -- --
## Region = South-eastern Asia 2 0.1622 [0.0344; 0.7657] 1.2541 1.1199
## Region = Southern Asia 2 0.1255 [0.0530; 0.2971] 0.3869 0.6220
## Region = Western Asia 6 0.5996 [0.2565; 1.4017] 1.1262 1.0612
## Region = Northern Africa 4 0.2230 [0.1296; 0.3839] 0.3071 0.5542
## Region = Southern Africa 1 0.2093 [0.2091; 0.2095] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 657578.79 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.5436 [0.5432; 0.5440] 0.3 1.5 Northern Africa
## ARGENTINA 0.8075 [0.8071; 0.8080] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 2.1429 [2.1419; 2.1439] 0.8 1.5 Australia and New Zealand
## AUSTRIA 3.8892 [3.8870; 3.8914] 0.6 1.5 Western Europe
## BELARUS 0.0209 [0.0208; 0.0211] 0.0 1.5 Eastern Europe
## BELGIUM 2.8197 [2.8180; 2.8213] 0.5 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1042 [0.1036; 0.1047] 0.0 1.5 Southern Europe
## BRAZIL 0.2003 [0.2002; 0.2004] 0.7 1.5 Central and South America and the Caribbean
## BULGARIA 0.3512 [0.3505; 0.3520] 0.0 1.5 Eastern Europe
## CANADA 6.4490 [6.4476; 6.4504] 3.8 1.5 Northern America
## CHILE 0.4029 [0.4024; 0.4033] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0100 [0.0100; 0.0101] 0.2 1.5 Eastern Asia
## COLOMBIA 0.0904 [0.0902; 0.0905] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.5290 [0.5279; 0.5302] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.4462 [2.4447; 2.4478] 0.4 1.5 Eastern Europe
## ECUADOR 0.3715 [0.3710; 0.3720] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.4519 [0.4517; 0.4522] 0.7 1.5 Northern Africa
## ESTONIA 0.6499 [0.6477; 0.6522] 0.0 1.5 Northern Europe
## FINLAND 6.2340 [6.2305; 6.2375] 0.6 1.5 Northern Europe
## FRANCE 4.6399 [4.6391; 4.6408] 5.0 1.5 Western Europe
## GERMANY 4.1898 [4.1890; 4.1905] 5.7 1.5 Western Europe
## GREECE 2.9461 [2.9444; 2.9478] 0.5 1.5 Southern Europe
## HUNGARY 1.3818 [1.3806; 1.3831] 0.2 1.5 Eastern Europe
## INDIA 0.0843 [0.0843; 0.0844] 1.8 1.5 Southern Asia
## IRELAND 5.7487 [5.7451; 5.7523] 0.4 1.5 Northern Europe
## ITALY 2.1488 [2.1482; 2.1494] 2.2 1.5 Southern Europe
## JAPAN 1.7794 [1.7790; 1.7798] 3.8 1.5 Eastern Asia
## JORDAN 0.2711 [0.2705; 0.2717] 0.0 1.5 Western Asia
## KAZAKHSTAN 0.0437 [0.0436; 0.0439] 0.0 1.5 Central Asia
## KUWAIT 0.3421 [0.3411; 0.3431] 0.0 1.5 Western Asia
## LATVIA 0.8875 [0.8853; 0.8896] 0.0 1.5 Northern Europe
## LEBANON 0.7933 [0.7921; 0.7945] 0.1 1.5 Western Asia
## LITHUANIA 0.8457 [0.8440; 0.8474] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.0584 [4.0494; 4.0674] 0.0 1.5 Western Europe
## MEXICO 0.2474 [0.2473; 0.2476] 0.5 1.5 Central and South America and the Caribbean
## MOROCCO 0.0653 [0.0651; 0.0654] 0.0 1.5 Northern Africa
## NETHERLANDS 2.8475 [2.8461; 2.8488] 0.8 1.5 Western Europe
## NEW ZEALAND 1.9467 [1.9446; 1.9489] 0.1 1.5 Australia and New Zealand
## NORWAY 4.8179 [4.8147; 4.8211] 0.4 1.5 Northern Europe
## PAKISTAN 0.2124 [0.2123; 0.2125] 0.7 1.5 Southern Asia
## PERU 0.0954 [0.0952; 0.0955] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0780 [0.0779; 0.0781] 0.1 1.5 South-eastern Asia
## POLAND 0.2180 [0.2177; 0.2182] 0.1 1.5 Eastern Europe
## PORTUGAL 4.3551 [4.3530; 4.3572] 0.8 1.5 Southern Europe
## PUERTO RICO 9.4847 [9.4794; 9.4900] 0.6 1.5 Central and South America and the Caribbean
## ROMANIA 0.7460 [0.7453; 0.7466] 0.3 1.5 Eastern Europe
## RUSSIA 0.3209 [0.3208; 0.3211] 0.8 1.5 Eastern Europe
## SAUDI ARABIA 0.7378 [0.7373; 0.7383] 0.4 1.5 Western Asia
## SERBIA 0.1341 [0.1337; 0.1345] 0.0 1.5 Southern Europe
## SLOVAKIA 3.2284 [3.2259; 3.2309] 0.3 1.5 Eastern Europe
## SLOVENIA 2.4452 [2.4416; 2.4487] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2379 [0.2377; 0.2381] 0.2 1.5 Southern Africa
## SOUTH KOREA 1.7305 [1.7299; 1.7311] 1.5 1.5 Eastern Asia
## SPAIN 5.7247 [5.7236; 5.7258] 4.5 1.5 Southern Europe
## SWEDEN 4.7299 [4.7276; 4.7322] 0.8 1.5 Northern Europe
## SWITZERLAND 2.9603 [2.9584; 2.9623] 0.4 1.5 Western Europe
## TAIWAN 0.2224 [0.2221; 0.2227] 0.1 1.5 Eastern Asia
## THAILAND 0.4123 [0.4121; 0.4126] 0.5 1.5 South-eastern Asia
## TUNISIA 0.4017 [0.4011; 0.4024] 0.1 1.5 Northern Africa
## TÜRKIYE 3.1476 [3.1470; 3.1483] 4.0 1.5 Western Asia
## UNITED ARAB EMIRATES 0.3576 [0.3569; 0.3582] 0.1 1.5 Western Asia
## UNITED KINGDOM 6.6897 [6.6887; 6.6908] 7.3 1.5 Northern Europe
## UNITED STATES 8.4560 [8.4555; 8.4565] 44.7 1.5 Northern America
## URUGUAY 1.0938 [1.0920; 1.0957] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 1.0497 [1.0491; 1.0503] 0.5 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.3669 [4.3667; 4.3671] 68686.77 0
## Random effects model 0.7995 [0.5972; 1.0702] -1.50 0.1326
##
## Quantifying heterogeneity:
## tau^2 = 1.4395 [1.3196; 3.6551]; tau = 1.1998 [1.1488; 1.9118]
## I^2 = 100.0%; H = 6176.17
##
## Test of heterogeneity:
## Q d.f. p-value
## 2441281374.51 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 8.2788 [8.2783; 8.2793] 5566887.46 100.0%
## Region = Central and South America and t ... 10 0.7172 [0.7170; 0.7173] 131928318.76 100.0%
## Region = Northern Europe 8 6.2225 [6.2216; 6.2233] 10598553.38 100.0%
## Region = Eastern Europe 8 0.8240 [0.8238; 0.8243] 46532134.40 100.0%
## Region = Southern Europe 8 3.9860 [3.9854; 3.9866] 42713840.25 100.0%
## Region = Western Europe 7 4.1278 [4.1273; 4.1283] 6567298.08 100.0%
## Region = Australia and New Zealand 2 2.1122 [2.1113; 2.1131] 24887.10 100.0%
## Region = Eastern Asia 4 1.3791 [1.3788; 1.3793] 136395208.96 100.0%
## Region = Central Asia 1 0.0437 [0.0436; 0.0439] 0.00 --
## Region = South-eastern Asia 2 0.2891 [0.2889; 0.2892] 6039855.91 100.0%
## Region = Southern Asia 2 0.1084 [0.1084; 0.1084] 9032148.30 100.0%
## Region = Western Asia 6 2.5887 [2.5882; 2.5892] 28374774.43 100.0%
## Region = Northern Africa 4 0.4454 [0.4452; 0.4456] 3237163.16 100.0%
## Region = Southern Africa 1 0.2379 [0.2377; 0.2381] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2014270304.31 13 0
## Within groups 427011070.20 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 7.3846 [5.6625; 9.6304] 0.0367 0.1916
## Region = Central and South America and t ... 10 0.4731 [0.1865; 1.2001] 2.2553 1.5018
## Region = Northern Europe 8 2.6805 [2.0952; 3.4292] 0.1264 0.3555
## Region = Eastern Europe 8 0.5044 [0.2321; 1.0959] 1.2542 1.1199
## Region = Southern Europe 8 1.1400 [0.7364; 1.7647] 0.3976 0.6306
## Region = Western Europe 7 3.5632 [3.0980; 4.0983] 0.0357 0.1889
## Region = Australia and New Zealand 2 2.0425 [1.8590; 2.2440] 0.0046 0.0679
## Region = Eastern Asia 4 0.2880 [0.0629; 1.3189] 2.4108 1.5527
## Region = Central Asia 1 0.0437 [0.0436; 0.0439] -- --
## Region = South-eastern Asia 2 0.1794 [0.0351; 0.9167] 1.3854 1.1770
## Region = Southern Asia 2 0.1339 [0.0541; 0.3310] 0.4266 0.6531
## Region = Western Asia 6 0.6276 [0.2510; 1.5694] 1.3121 1.1455
## Region = Northern Africa 4 0.2833 [0.1744; 0.4603] 0.2453 0.4953
## Region = Southern Africa 1 0.2379 [0.2377; 0.2381] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 730162.36 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.6123 [0.6119; 0.6128] 0.3 1.5 Northern Africa
## ARGENTINA 0.9655 [0.9650; 0.9660] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 3.9071 [3.9058; 3.9084] 1.3 1.5 Australia and New Zealand
## AUSTRIA 4.2035 [4.2012; 4.2058] 0.5 1.5 Western Europe
## BELARUS 0.0344 [0.0342; 0.0346] 0.0 1.5 Eastern Europe
## BELGIUM 2.8712 [2.8696; 2.8729] 0.5 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1485 [0.1479; 0.1492] 0.0 1.5 Southern Europe
## BRAZIL 0.2464 [0.2463; 0.2465] 0.7 1.5 Central and South America and the Caribbean
## BULGARIA 0.4812 [0.4804; 0.4820] 0.1 1.5 Eastern Europe
## CANADA 7.3987 [7.3972; 7.4002] 3.8 1.5 Northern America
## CHILE 0.4888 [0.4882; 0.4893] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0143 [0.0143; 0.0143] 0.3 1.5 Eastern Asia
## COLOMBIA 0.1060 [0.1058; 0.1062] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.5611 [0.5599; 0.5622] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 2.8554 [2.8537; 2.8570] 0.4 1.5 Eastern Europe
## ECUADOR 0.3806 [0.3801; 0.3811] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.5793 [0.5791; 0.5796] 0.7 1.5 Northern Africa
## ESTONIA 0.8479 [0.8453; 0.8505] 0.0 1.5 Northern Europe
## FINLAND 6.7770 [6.7733; 6.7806] 0.5 1.5 Northern Europe
## FRANCE 4.9101 [4.9092; 4.9110] 4.6 1.5 Western Europe
## GERMANY 4.4645 [4.4637; 4.4653] 5.3 1.5 Western Europe
## GREECE 3.2543 [3.2525; 3.2561] 0.5 1.5 Southern Europe
## HUNGARY 1.6067 [1.6054; 1.6080] 0.2 1.5 Eastern Europe
## INDIA 0.0874 [0.0874; 0.0874] 1.6 1.5 Southern Asia
## IRELAND 6.9816 [6.9776; 6.9855] 0.5 1.5 Northern Europe
## ITALY 2.2892 [2.2886; 2.2899] 2.0 1.5 Southern Europe
## JAPAN 2.3035 [2.3031; 2.3040] 4.3 1.5 Eastern Asia
## JORDAN 0.4185 [0.4177; 0.4192] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0488 [0.0487; 0.0490] 0.0 1.5 Central Asia
## KUWAIT 0.7306 [0.7291; 0.7321] 0.0 1.5 Western Asia
## LATVIA 1.1425 [1.1401; 1.1449] 0.0 1.5 Northern Europe
## LEBANON 0.8309 [0.8297; 0.8321] 0.1 1.5 Western Asia
## LITHUANIA 1.0202 [1.0183; 1.0221] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.0818 [4.0729; 4.0907] 0.0 1.5 Western Europe
## MEXICO 0.2527 [0.2525; 0.2528] 0.4 1.5 Central and South America and the Caribbean
## MOROCCO 0.0781 [0.0780; 0.0783] 0.0 1.5 Northern Africa
## NETHERLANDS 3.0818 [3.0804; 3.0832] 0.8 1.5 Western Europe
## NEW ZEALAND 2.2133 [2.2110; 2.2155] 0.1 1.5 Australia and New Zealand
## NORWAY 4.9931 [4.9899; 4.9963] 0.4 1.5 Northern Europe
## PAKISTAN 0.2292 [0.2291; 0.2293] 0.6 1.5 Southern Asia
## PERU 0.1101 [0.1099; 0.1103] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0914 [0.0913; 0.0915] 0.1 1.5 South-eastern Asia
## POLAND 0.2820 [0.2817; 0.2822] 0.2 1.5 Eastern Europe
## PORTUGAL 4.3602 [4.3581; 4.3623] 0.7 1.5 Southern Europe
## PUERTO RICO 9.6517 [9.6463; 9.6571] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 0.8871 [0.8865; 0.8878] 0.3 1.5 Eastern Europe
## RUSSIA 0.7291 [0.7288; 0.7293] 1.5 1.5 Eastern Europe
## SAUDI ARABIA 1.0581 [1.0575; 1.0587] 0.5 1.5 Western Asia
## SERBIA 0.2477 [0.2471; 0.2482] 0.0 1.5 Southern Europe
## SLOVAKIA 3.4486 [3.4460; 3.4511] 0.3 1.5 Eastern Europe
## SLOVENIA 2.6081 [2.6045; 2.6118] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2529 [0.2527; 0.2532] 0.2 1.5 Southern Africa
## SOUTH KOREA 1.8725 [1.8719; 1.8731] 1.4 1.5 Eastern Asia
## SPAIN 6.0784 [6.0772; 6.0795] 4.2 1.5 Southern Europe
## SWEDEN 5.1810 [5.1786; 5.1834] 0.7 1.5 Northern Europe
## SWITZERLAND 3.1810 [3.1789; 3.1830] 0.4 1.5 Western Europe
## TAIWAN 0.2939 [0.2935; 0.2942] 0.1 1.5 Eastern Asia
## THAILAND 0.5597 [0.5594; 0.5600] 0.6 1.5 South-eastern Asia
## TUNISIA 0.4665 [0.4658; 0.4671] 0.1 1.5 Northern Africa
## TÜRKIYE 3.5727 [3.5720; 3.5734] 4.0 1.5 Western Asia
## UNITED ARAB EMIRATES 0.5663 [0.5655; 0.5671] 0.1 1.5 Western Asia
## UNITED KINGDOM 8.0419 [8.0408; 8.0431] 7.6 1.5 Northern Europe
## UNITED STATES 9.5835 [9.5830; 9.5841] 44.2 1.5 Northern America
## URUGUAY 1.1789 [1.1770; 1.1809] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 1.2948 [1.2941; 1.2954] 0.6 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.8683 [4.8681; 4.8685] 79244.65 0
## Random effects model 0.9651 [0.7220; 1.2901] -0.24 0.8104
##
## Quantifying heterogeneity:
## tau^2 = 1.4252 [1.2649; 3.4746]; tau = 1.1938 [1.1247; 1.8640]
## I^2 = 100.0%; H = 6622.16
##
## Test of heterogeneity:
## Q d.f. p-value
## 2806590083.64 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 9.3889 [9.3884; 9.3894] 5879179.99 100.0%
## Region = Central and South America and t ... 10 0.7781 [0.7780; 0.7783] 134239288.05 100.0%
## Region = Northern Europe 8 7.3753 [7.3743; 7.3762] 14507032.36 100.0%
## Region = Eastern Europe 8 1.0490 [1.0488; 1.0493] 36007055.41 100.0%
## Region = Southern Europe 8 4.1939 [4.1933; 4.1945] 46228063.10 100.0%
## Region = Western Europe 7 4.3859 [4.3854; 4.3864] 6962819.11 100.0%
## Region = Australia and New Zealand 2 3.6930 [3.6918; 3.6942] 1062843.73 100.0%
## Region = Eastern Asia 4 1.6667 [1.6664; 1.6670] 183907345.40 100.0%
## Region = Central Asia 1 0.0488 [0.0487; 0.0490] 0.00 --
## Region = South-eastern Asia 2 0.3956 [0.3954; 0.3958] 8764573.50 100.0%
## Region = Southern Asia 2 0.1146 [0.1146; 0.1147] 10690428.98 100.0%
## Region = Western Asia 6 2.8948 [2.8943; 2.8953] 30664877.46 100.0%
## Region = Northern Africa 4 0.5446 [0.5444; 0.5448] 3860815.37 100.0%
## Region = Southern Africa 1 0.2529 [0.2527; 0.2532] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2323815761.20 13 0
## Within groups 482774322.44 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 8.4206 [6.5347; 10.8507] 0.0335 0.1830
## Region = Central and South America and t ... 10 0.5350 [0.2228; 1.2851] 1.9988 1.4138
## Region = Northern Europe 8 3.1514 [2.4032; 4.1325] 0.1530 0.3911
## Region = Eastern Europe 8 0.6837 [0.3806; 1.2280] 0.7143 0.8452
## Region = Southern Europe 8 1.3437 [0.8645; 2.0886] 0.4051 0.6365
## Region = Western Europe 7 3.7581 [3.2691; 4.3202] 0.0354 0.1882
## Region = Australia and New Zealand 2 2.9407 [1.6849; 5.1325] 0.1615 0.4019
## Region = Eastern Asia 4 0.3669 [0.0727; 1.8516] 2.7285 1.6518
## Region = Central Asia 1 0.0488 [0.0487; 0.0490] -- --
## Region = South-eastern Asia 2 0.2261 [0.0383; 1.3361] 1.6429 1.2817
## Region = Southern Asia 2 0.1415 [0.0550; 0.3640] 0.4646 0.6816
## Region = Western Asia 6 0.9035 [0.4088; 1.9964] 0.9819 0.9909
## Region = Northern Africa 4 0.3372 [0.2079; 0.5469] 0.2434 0.4934
## Region = Southern Africa 1 0.2529 [0.2527; 0.2532] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 776898.88 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 0.8777 [ 0.8772; 0.8782] 0.4 1.5 Northern Africa
## ARGENTINA 1.0803 [ 1.0798; 1.0808] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 6.1076 [ 6.1060; 6.1093] 1.9 1.5 Australia and New Zealand
## AUSTRIA 4.5047 [ 4.5024; 4.5071] 0.5 1.5 Western Europe
## BELARUS 0.0451 [ 0.0449; 0.0453] 0.0 1.5 Eastern Europe
## BELGIUM 3.1127 [ 3.1110; 3.1144] 0.5 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.1600 [ 0.1593; 0.1607] 0.0 1.5 Southern Europe
## BRAZIL 0.2972 [ 0.2971; 0.2973] 0.8 1.5 Central and South America and the Caribbean
## BULGARIA 0.5914 [ 0.5905; 0.5924] 0.1 1.5 Eastern Europe
## CANADA 8.1048 [ 8.1032; 8.1063] 3.8 1.5 Northern America
## CHILE 0.5859 [ 0.5853; 0.5865] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0183 [ 0.0183; 0.0184] 0.3 1.5 Eastern Asia
## COLOMBIA 0.1232 [ 0.1230; 0.1233] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.6205 [ 0.6193; 0.6218] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 3.2558 [ 3.2540; 3.2576] 0.4 1.5 Eastern Europe
## ECUADOR 0.4213 [ 0.4207; 0.4218] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.7263 [ 0.7261; 0.7266] 0.9 1.5 Northern Africa
## ESTONIA 1.0802 [ 1.0772; 1.0831] 0.0 1.5 Northern Europe
## FINLAND 6.5386 [ 6.5351; 6.5422] 0.5 1.5 Northern Europe
## FRANCE 5.2787 [ 5.2778; 5.2797] 4.4 1.5 Western Europe
## GERMANY 4.7110 [ 4.7102; 4.7117] 5.0 1.5 Western Europe
## GREECE 3.2833 [ 3.2815; 3.2851] 0.5 1.5 Southern Europe
## HUNGARY 1.7894 [ 1.7880; 1.7908] 0.2 1.5 Eastern Europe
## INDIA 0.0949 [ 0.0949; 0.0949] 1.6 1.5 Southern Asia
## IRELAND 7.5993 [ 7.5952; 7.6035] 0.5 1.5 Northern Europe
## ITALY 2.4579 [ 2.4572; 2.4585] 1.9 1.5 Southern Europe
## JAPAN 2.9007 [ 2.9002; 2.9012] 4.8 1.5 Eastern Asia
## JORDAN 0.5784 [ 0.5776; 0.5793] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0671 [ 0.0669; 0.0673] 0.0 1.5 Central Asia
## KUWAIT 0.6781 [ 0.6767; 0.6794] 0.0 1.5 Western Asia
## LATVIA 1.3962 [ 1.3935; 1.3989] 0.0 1.5 Northern Europe
## LEBANON 0.9179 [ 0.9167; 0.9192] 0.1 1.5 Western Asia
## LITHUANIA 1.2046 [ 1.2026; 1.2067] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.2040 [ 4.1951; 4.2129] 0.0 1.5 Western Europe
## MEXICO 0.2758 [ 0.2756; 0.2759] 0.4 1.5 Central and South America and the Caribbean
## MOROCCO 0.0859 [ 0.0858; 0.0861] 0.0 1.5 Northern Africa
## NETHERLANDS 3.3712 [ 3.3698; 3.3727] 0.7 1.5 Western Europe
## NEW ZEALAND 2.5248 [ 2.5224; 2.5272] 0.2 1.5 Australia and New Zealand
## NORWAY 5.0716 [ 5.0683; 5.0748] 0.3 1.5 Northern Europe
## PAKISTAN 0.2485 [ 0.2483; 0.2486] 0.6 1.5 Southern Asia
## PERU 0.1205 [ 0.1203; 0.1207] 0.0 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.0950 [ 0.0949; 0.0951] 0.1 1.5 South-eastern Asia
## POLAND 0.3720 [ 0.3717; 0.3723] 0.2 1.5 Eastern Europe
## PORTUGAL 4.8798 [ 4.8776; 4.8821] 0.7 1.5 Southern Europe
## PUERTO RICO 11.3900 [11.3841; 11.3959] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 1.0166 [ 1.0159; 1.0174] 0.3 1.5 Eastern Europe
## RUSSIA 0.9373 [ 0.9370; 0.9376] 1.8 1.5 Eastern Europe
## SAUDI ARABIA 1.3930 [ 1.3923; 1.3936] 0.6 1.5 Western Asia
## SERBIA 0.3969 [ 0.3962; 0.3976] 0.0 1.5 Southern Europe
## SLOVAKIA 3.7363 [ 3.7336; 3.7390] 0.3 1.5 Eastern Europe
## SLOVENIA 2.9582 [ 2.9543; 2.9621] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2727 [ 0.2724; 0.2729] 0.2 1.5 Southern Africa
## SOUTH KOREA 2.0337 [ 2.0331; 2.0344] 1.3 1.5 Eastern Asia
## SPAIN 6.3670 [ 6.3658; 6.3682] 3.9 1.5 Southern Europe
## SWEDEN 5.6087 [ 5.6063; 5.6112] 0.7 1.5 Northern Europe
## SWITZERLAND 3.4305 [ 3.4284; 3.4326] 0.4 1.5 Western Europe
## TAIWAN 0.3606 [ 0.3602; 0.3610] 0.1 1.5 Eastern Asia
## THAILAND 0.6334 [ 0.6331; 0.6337] 0.6 1.5 South-eastern Asia
## TUNISIA 0.5206 [ 0.5199; 0.5213] 0.1 1.5 Northern Africa
## TÜRKIYE 4.0912 [ 4.0905; 4.0920] 4.1 1.5 Western Asia
## UNITED ARAB EMIRATES 0.8103 [ 0.8094; 0.8113] 0.1 1.5 Western Asia
## UNITED KINGDOM 9.5420 [ 9.5407; 9.5432] 8.1 1.5 Northern Europe
## UNITED STATES 10.2551 [10.2546; 10.2557] 42.6 1.5 Northern America
## URUGUAY 1.3733 [ 1.3712; 1.3753] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 1.5927 [ 1.5919; 1.5934] 0.6 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.2538 [5.2536; 5.2540] 87794.11 0
## Random effects model 1.1159 [0.8385; 1.4850] 0.75 0.4521
##
## Quantifying heterogeneity:
## tau^2 = 1.3821 [1.2326; 3.3581]; tau = 1.1756 [1.1102; 1.8325]
## I^2 = 100.0%; H = 6947.47
##
## Test of heterogeneity:
## Q d.f. p-value
## 3089108590.44 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 10.0609 [10.0603; 10.0614] 5371590.50 100.0%
## Region = Central and South America and t ... 10 0.9111 [ 0.9109; 0.9112] 158912021.44 100.0%
## Region = Northern Europe 8 8.5849 [ 8.5839; 8.5859] 20615913.66 100.0%
## Region = Eastern Europe 8 1.2323 [ 1.2320; 1.2325] 35846862.79 100.0%
## Region = Southern Europe 8 4.4051 [ 4.4045; 4.4057] 48295863.53 100.0%
## Region = Western Europe 7 4.6796 [ 4.6791; 4.6801] 7181921.65 100.0%
## Region = Australia and New Zealand 2 5.7207 [ 5.7192; 5.7222] 3043431.34 100.0%
## Region = Eastern Asia 4 2.0197 [ 2.0194; 2.0200] 234259188.02 100.0%
## Region = Central Asia 1 0.0671 [ 0.0669; 0.0673] 0.00 --
## Region = South-eastern Asia 2 0.4497 [ 0.4495; 0.4499] 10286375.38 100.0%
## Region = Southern Asia 2 0.1246 [ 0.1246; 0.1246] 11770844.51 100.0%
## Region = Western Asia 6 3.2999 [ 3.2993; 3.3004] 33849497.76 100.0%
## Region = Northern Africa 4 0.7149 [ 0.7147; 0.7151] 5559722.82 100.0%
## Region = Southern Africa 1 0.2727 [ 0.2724; 0.2729] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2514115357.03 13 0
## Within groups 574993233.40 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 9.1168 [7.2391; 11.4814] 0.0277 0.1664
## Region = Central and South America and t ... 10 0.6176 [0.2561; 1.4898] 2.0181 1.4206
## Region = Northern Europe 8 3.5371 [2.5886; 4.8332] 0.2030 0.4505
## Region = Eastern Europe 8 0.8207 [0.4790; 1.4064] 0.6040 0.7772
## Region = Southern Europe 8 1.5249 [0.9866; 2.3570] 0.3949 0.6284
## Region = Western Europe 7 4.0196 [3.5069; 4.6072] 0.0339 0.1842
## Region = Australia and New Zealand 2 3.9269 [1.6523; 9.3329] 0.3902 0.6246
## Region = Eastern Asia 4 0.4445 [0.0818; 2.4155] 2.9836 1.7273
## Region = Central Asia 1 0.0671 [0.0669; 0.0673] -- --
## Region = South-eastern Asia 2 0.2453 [0.0382; 1.5742] 1.7990 1.3413
## Region = Southern Asia 2 0.1536 [0.0598; 0.3943] 0.4631 0.6805
## Region = Western Asia 6 1.0884 [0.5255; 2.2543] 0.8281 0.9100
## Region = Northern Africa 4 0.4110 [0.2484; 0.6800] 0.2640 0.5138
## Region = Southern Africa 1 0.2727 [0.2724; 0.2729] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 776206.87 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 1.0435 [ 1.0430; 1.0440] 0.5 1.5 Northern Africa
## ARGENTINA 1.2359 [ 1.2353; 1.2364] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 7.5188 [ 7.5170; 7.5207] 2.1 1.5 Australia and New Zealand
## AUSTRIA 4.6332 [ 4.6308; 4.6355] 0.5 1.5 Western Europe
## BELARUS 0.0609 [ 0.0607; 0.0612] 0.0 1.5 Eastern Europe
## BELGIUM 3.4623 [ 3.4605; 3.4641] 0.5 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.2186 [ 0.2178; 0.2194] 0.0 1.5 Southern Europe
## BRAZIL 0.3671 [ 0.3669; 0.3672] 0.9 1.5 Central and South America and the Caribbean
## BULGARIA 0.6868 [ 0.6858; 0.6878] 0.1 1.5 Eastern Europe
## CANADA 8.9837 [ 8.9821; 8.9854] 3.8 1.5 Northern America
## CHILE 0.6971 [ 0.6964; 0.6977] 0.1 1.5 Central and South America and the Caribbean
## CHINA 0.0236 [ 0.0236; 0.0236] 0.4 1.5 Eastern Asia
## COLOMBIA 0.1650 [ 0.1648; 0.1652] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 0.7200 [ 0.7187; 0.7214] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 3.8896 [ 3.8876; 3.8915] 0.5 1.5 Eastern Europe
## ECUADOR 0.4820 [ 0.4815; 0.4826] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 0.9495 [ 0.9492; 0.9498] 1.0 1.5 Northern Africa
## ESTONIA 1.3609 [ 1.3576; 1.3642] 0.0 1.5 Northern Europe
## FINLAND 6.6345 [ 6.6310; 6.6381] 0.4 1.5 Northern Europe
## FRANCE 5.5188 [ 5.5179; 5.5198] 4.2 1.5 Western Europe
## GERMANY 4.9624 [ 4.9616; 4.9632] 4.7 1.5 Western Europe
## GREECE 3.3903 [ 3.3884; 3.3921] 0.4 1.5 Southern Europe
## HUNGARY 1.7720 [ 1.7706; 1.7734] 0.2 1.5 Eastern Europe
## INDIA 0.1004 [ 0.1004; 0.1005] 1.5 1.5 Southern Asia
## IRELAND 9.3143 [ 9.3098; 9.3189] 0.5 1.5 Northern Europe
## ITALY 2.5593 [ 2.5587; 2.5600] 1.8 1.5 Southern Europe
## JAPAN 3.1521 [ 3.1516; 3.1526] 4.7 1.5 Eastern Asia
## JORDAN 0.5796 [ 0.5788; 0.5804] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0902 [ 0.0899; 0.0904] 0.0 1.5 Central Asia
## KUWAIT 0.7833 [ 0.7818; 0.7847] 0.0 1.5 Western Asia
## LATVIA 1.6802 [ 1.6772; 1.6832] 0.0 1.5 Northern Europe
## LEBANON 1.0485 [ 1.0472; 1.0498] 0.1 1.5 Western Asia
## LITHUANIA 1.2834 [ 1.2812; 1.2855] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.1852 [ 4.1764; 4.1940] 0.0 1.5 Western Europe
## MEXICO 0.2998 [ 0.2996; 0.2999] 0.4 1.5 Central and South America and the Caribbean
## MOROCCO 0.1033 [ 0.1032; 0.1035] 0.0 1.5 Northern Africa
## NETHERLANDS 3.5567 [ 3.5552; 3.5582] 0.7 1.5 Western Europe
## NEW ZEALAND 2.9494 [ 2.9468; 2.9520] 0.2 1.5 Australia and New Zealand
## NORWAY 5.6805 [ 5.6771; 5.6839] 0.3 1.5 Northern Europe
## PAKISTAN 0.2817 [ 0.2816; 0.2818] 0.7 1.5 Southern Asia
## PERU 0.1993 [ 0.1991; 0.1996] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1056 [ 0.1055; 0.1057] 0.1 1.5 South-eastern Asia
## POLAND 0.4692 [ 0.4688; 0.4696] 0.2 1.5 Eastern Europe
## PORTUGAL 5.0680 [ 5.0657; 5.0703] 0.6 1.5 Southern Europe
## PUERTO RICO 12.9824 [12.9761; 12.9888] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 1.1237 [ 1.1229; 1.1244] 0.3 1.5 Eastern Europe
## RUSSIA 0.8413 [ 0.8411; 0.8416] 1.4 1.5 Eastern Europe
## SAUDI ARABIA 1.2320 [ 1.2313; 1.2326] 0.5 1.5 Western Asia
## SERBIA 0.7818 [ 0.7808; 0.7827] 0.1 1.5 Southern Europe
## SLOVAKIA 4.1169 [ 4.1141; 4.1198] 0.3 1.5 Eastern Europe
## SLOVENIA 3.2621 [ 3.2581; 3.2662] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.2909 [ 0.2907; 0.2912] 0.2 1.5 Southern Africa
## SOUTH KOREA 2.1810 [ 2.1803; 2.1817] 1.3 1.5 Eastern Asia
## SPAIN 6.4617 [ 6.4605; 6.4629] 3.5 1.5 Southern Europe
## SWEDEN 6.0197 [ 6.0172; 6.0223] 0.7 1.5 Northern Europe
## SWITZERLAND 3.7244 [ 3.7222; 3.7265] 0.4 1.5 Western Europe
## TAIWAN 0.4099 [ 0.4094; 0.4103] 0.1 1.5 Eastern Asia
## THAILAND 0.6688 [ 0.6685; 0.6691] 0.5 1.5 South-eastern Asia
## TUNISIA 0.5660 [ 0.5653; 0.5668] 0.1 1.5 Northern Africa
## TÜRKIYE 4.6908 [ 4.6900; 4.6915] 4.3 1.5 Western Asia
## UNITED ARAB EMIRATES 1.0947 [ 1.0936; 1.0958] 0.1 1.5 Western Asia
## UNITED KINGDOM 11.0046 [11.0033; 11.0059] 8.5 1.5 Northern Europe
## UNITED STATES 11.6350 [11.6343; 11.6356] 43.6 1.5 Northern America
## URUGUAY 1.4897 [ 1.4876; 1.4919] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 1.0882 [ 1.0875; 1.0888] 0.4 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.9048 [5.9046; 5.9050] 99352.31 0
## Random effects model 1.2656 [0.9445; 1.6958] 1.58 0.1147
##
## Quantifying heterogeneity:
## tau^2 = 1.4489 [1.2407; 3.3792]; tau = 1.2037 [1.1139; 1.8383]
## I^2 = 100.0%; H = 7483.56
##
## Test of heterogeneity:
## Q d.f. p-value
## 3584233012.20 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 11.3974 [11.3968; 11.3980] 7274660.37 100.0%
## Region = Central and South America and t ... 10 0.9156 [ 0.9154; 0.9157] 169918775.40 100.0%
## Region = Northern Europe 8 9.8630 [ 9.8619; 9.8641] 25954476.21 100.0%
## Region = Eastern Europe 8 1.2840 [ 1.2837; 1.2842] 47624760.96 100.0%
## Region = Southern Europe 8 4.4639 [ 4.4633; 4.4645] 48035137.06 100.0%
## Region = Western Europe 7 4.9182 [ 4.9177; 4.9187] 6754573.35 100.0%
## Region = Australia and New Zealand 2 7.0400 [ 7.0384; 7.0416] 4047256.53 100.0%
## Region = Eastern Asia 4 2.1122 [ 2.1119; 2.1125] 278093811.93 100.0%
## Region = Central Asia 1 0.0902 [ 0.0899; 0.0904] 0.00 --
## Region = South-eastern Asia 2 0.4709 [ 0.4707; 0.4711] 10868778.15 100.0%
## Region = Southern Asia 2 0.1367 [ 0.1367; 0.1368] 15290624.25 100.0%
## Region = Western Asia 6 3.7762 [ 3.7756; 3.7768] 43626433.26 100.0%
## Region = Northern Africa 4 0.9010 [ 0.9007; 0.9012] 7049513.42 100.0%
## Region = Southern Africa 1 0.2909 [ 0.2907; 0.2912] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2919694211.31 13 0
## Within groups 664538800.89 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 10.2238 [7.9351; 13.1726] 0.0334 0.1829
## Region = Central and South America and t ... 10 0.7080 [0.2954; 1.6965] 1.9882 1.4100
## Region = Northern Europe 8 4.0203 [2.8863; 5.5997] 0.2287 0.4782
## Region = Eastern Europe 8 0.9230 [0.5078; 1.6775] 0.7434 0.8622
## Region = Southern Europe 8 1.8078 [1.1829; 2.7627] 0.3746 0.6120
## Region = Western Europe 7 4.2331 [3.7225; 4.8136] 0.0301 0.1735
## Region = Australia and New Zealand 2 4.7091 [1.8822; 11.7821] 0.4379 0.6617
## Region = Eastern Asia 4 0.5078 [0.0889; 2.9006] 3.1618 1.7781
## Region = Central Asia 1 0.0902 [0.0899; 0.0904] -- --
## Region = South-eastern Asia 2 0.2657 [0.0435; 1.6221] 1.7037 1.3053
## Region = Southern Asia 2 0.1682 [0.0612; 0.4621] 0.5317 0.7292
## Region = Western Asia 6 1.2017 [0.5323; 2.7129] 1.0357 1.0177
## Region = Northern Africa 4 0.4907 [0.2944; 0.8177] 0.2717 0.5212
## Region = Southern Africa 1 0.2909 [0.2907; 0.2912] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 726019.25 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 1.4666 [ 1.4659; 1.4672] 0.6 1.5 Northern Africa
## ARGENTINA 1.2701 [ 1.2696; 1.2707] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 8.6392 [ 8.6372; 8.6411] 2.2 1.5 Australia and New Zealand
## AUSTRIA 5.5558 [ 5.5533; 5.5584] 0.5 1.5 Western Europe
## BELARUS 0.0745 [ 0.0742; 0.0748] 0.0 1.5 Eastern Europe
## BELGIUM 4.6625 [ 4.6604; 4.6645] 0.6 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.3015 [ 0.3006; 0.3025] 0.0 1.5 Southern Europe
## BRAZIL 0.4401 [ 0.4399; 0.4402] 1.0 1.5 Central and South America and the Caribbean
## BULGARIA 0.9792 [ 0.9780; 0.9804] 0.1 1.5 Eastern Europe
## CANADA 9.9954 [ 9.9937; 9.9971] 3.9 1.5 Northern America
## CHILE 0.7907 [ 0.7900; 0.7914] 0.2 1.5 Central and South America and the Caribbean
## CHINA 0.0286 [ 0.0286; 0.0286] 0.4 1.5 Eastern Asia
## COLOMBIA 0.1667 [ 0.1665; 0.1669] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 1.0827 [ 1.0810; 1.0843] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 4.9366 [ 4.9344; 4.9388] 0.6 1.5 Eastern Europe
## ECUADOR 0.4980 [ 0.4975; 0.4986] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 1.3411 [ 1.3407; 1.3414] 1.3 1.5 Northern Africa
## ESTONIA 1.7473 [ 1.7435; 1.7510] 0.0 1.5 Northern Europe
## FINLAND 7.1434 [ 7.1397; 7.1470] 0.4 1.5 Northern Europe
## FRANCE 5.6502 [ 5.6492; 5.6511] 3.9 1.5 Western Europe
## GERMANY 5.2710 [ 5.2702; 5.2718] 4.6 1.5 Western Europe
## GREECE 3.9338 [ 3.9318; 3.9358] 0.4 1.5 Southern Europe
## HUNGARY 1.9009 [ 1.8995; 1.9024] 0.2 1.5 Eastern Europe
## INDIA 0.1038 [ 0.1038; 0.1038] 1.5 1.5 Southern Asia
## IRELAND 10.0262 [10.0215; 10.0310] 0.5 1.5 Northern Europe
## ITALY 2.6128 [ 2.6122; 2.6135] 1.7 1.5 Southern Europe
## JAPAN 3.4593 [ 3.4587; 3.4598] 4.7 1.5 Eastern Asia
## JORDAN 0.8999 [ 0.8989; 0.9009] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0727 [ 0.0725; 0.0729] 0.0 1.5 Central Asia
## KUWAIT 1.3133 [ 1.3114; 1.3152] 0.1 1.5 Western Asia
## LATVIA 1.8527 [ 1.8495; 1.8558] 0.0 1.5 Northern Europe
## LEBANON 1.3224 [ 1.3210; 1.3239] 0.1 1.5 Western Asia
## LITHUANIA 1.6490 [ 1.6465; 1.6514] 0.1 1.5 Northern Europe
## LUXEMBOURG 4.3268 [ 4.3179; 4.3357] 0.0 1.5 Western Europe
## MEXICO 0.3259 [ 0.3257; 0.3260] 0.4 1.5 Central and South America and the Caribbean
## MOROCCO 0.1130 [ 0.1129; 0.1132] 0.0 1.5 Northern Africa
## NETHERLANDS 3.7671 [ 3.7656; 3.7687] 0.7 1.5 Western Europe
## NEW ZEALAND 3.4626 [ 3.4598; 3.4654] 0.2 1.5 Australia and New Zealand
## NORWAY 5.8838 [ 5.8804; 5.8872] 0.3 1.5 Northern Europe
## PAKISTAN 0.3136 [ 0.3135; 0.3137] 0.7 1.5 Southern Asia
## PERU 0.2131 [ 0.2128; 0.2134] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1206 [ 0.1205; 0.1207] 0.1 1.5 South-eastern Asia
## POLAND 0.7797 [ 0.7792; 0.7801] 0.3 1.5 Eastern Europe
## PORTUGAL 4.9211 [ 4.9189; 4.9233] 0.5 1.5 Southern Europe
## PUERTO RICO 14.3848 [14.3780; 14.3916] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 1.3004 [ 1.2996; 1.3013] 0.3 1.5 Eastern Europe
## RUSSIA 0.2778 [ 0.2776; 0.2779] 0.4 1.5 Eastern Europe
## SAUDI ARABIA 1.3045 [ 1.3039; 1.3052] 0.5 1.5 Western Asia
## SERBIA 1.1215 [ 1.1203; 1.1226] 0.1 1.5 Southern Europe
## SLOVAKIA 5.0693 [ 5.0662; 5.0724] 0.3 1.5 Eastern Europe
## SLOVENIA 3.6342 [ 3.6299; 3.6385] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.3238 [ 0.3236; 0.3241] 0.2 1.5 Southern Africa
## SOUTH KOREA 2.4374 [ 2.4367; 2.4381] 1.3 1.5 Eastern Asia
## SPAIN 6.8250 [ 6.8237; 6.8262] 3.4 1.5 Southern Europe
## SWEDEN 6.6761 [ 6.6734; 6.6787] 0.7 1.5 Northern Europe
## SWITZERLAND 3.9619 [ 3.9597; 3.9641] 0.4 1.5 Western Europe
## TAIWAN 0.4897 [ 0.4893; 0.4902] 0.1 1.5 Eastern Asia
## THAILAND 0.7037 [ 0.7034; 0.7040] 0.5 1.5 South-eastern Asia
## TUNISIA 0.6241 [ 0.6234; 0.6249] 0.1 1.5 Northern Africa
## TÜRKIYE 5.0234 [ 5.0226; 5.0242] 4.3 1.5 Western Asia
## UNITED ARAB EMIRATES 0.7763 [ 0.7753; 0.7772] 0.1 1.5 Western Asia
## UNITED KINGDOM 12.5535 [12.5521; 12.5549] 8.9 1.5 Northern Europe
## UNITED STATES 12.7740 [12.7734; 12.7747] 43.9 1.5 Northern America
## URUGUAY 1.5550 [ 1.5528; 1.5572] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.6046 [ 0.6041; 0.6051] 0.2 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.5643 [6.5641; 6.5645] 110256.30 0
## Random effects model 1.4086 [1.0497; 1.8904] 2.28 0.0224
##
## Quantifying heterogeneity:
## tau^2 = 1.4640 [1.2644; 3.4394]; tau = 1.2100 [1.1245; 1.8546]
## I^2 = 100.0%; H = 7862.69
##
## Test of heterogeneity:
## Q d.f. p-value
## 3956604647.10 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 12.5227 [12.5221; 12.5233] 7344433.23 100.0%
## Region = Central and South America and t ... 10 0.9316 [ 0.9315; 0.9318] 182980809.37 100.0%
## Region = Northern Europe 8 11.1807 [11.1796; 11.1818] 31775941.84 100.0%
## Region = Eastern Europe 8 1.5317 [ 1.5313; 1.5320] 91982088.94 100.0%
## Region = Southern Europe 8 4.6598 [ 4.6592; 4.6604] 50668219.57 100.0%
## Region = Western Europe 7 5.2185 [ 5.2179; 5.2190] 4608048.38 100.0%
## Region = Australia and New Zealand 2 8.0927 [ 8.0910; 8.0945] 4573784.08 100.0%
## Region = Eastern Asia 4 2.2714 [ 2.2711; 2.2717] 321455588.13 100.0%
## Region = Central Asia 1 0.0727 [ 0.0725; 0.0729] 0.00 --
## Region = South-eastern Asia 2 0.4903 [ 0.4901; 0.4905] 11296697.38 100.0%
## Region = Southern Asia 2 0.1474 [ 0.1473; 0.1474] 19473545.67 100.0%
## Region = Western Asia 6 4.0303 [ 4.0297; 4.0309] 47448853.12 100.0%
## Region = Northern Africa 4 1.2754 [ 1.2751; 1.2757] 10372798.41 100.0%
## Region = Southern Africa 1 0.3238 [ 0.3236; 0.3241] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 3172623838.99 13 0
## Within groups 783980808.11 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 11.2996 [8.8852; 14.3701] 0.0301 0.1734
## Region = Central and South America and t ... 10 0.7141 [0.2917; 1.7482] 2.0870 1.4447
## Region = Northern Europe 8 4.5648 [3.2182; 6.4748] 0.2545 0.5044
## Region = Eastern Europe 8 0.9971 [0.4257; 2.3353] 1.5083 1.2281
## Region = Southern Europe 8 2.1516 [1.4088; 3.2862] 0.3735 0.6112
## Region = Western Europe 7 4.6888 [4.2350; 5.1912] 0.0189 0.1374
## Region = Australia and New Zealand 2 5.4694 [2.2326; 13.3986] 0.4180 0.6465
## Region = Eastern Asia 4 0.5862 [0.1014; 3.3900] 3.2067 1.7907
## Region = Central Asia 1 0.0727 [0.0725; 0.0729] -- --
## Region = South-eastern Asia 2 0.2913 [0.0517; 1.6408] 1.5556 1.2472
## Region = Southern Asia 2 0.1804 [0.0610; 0.5332] 0.6114 0.7819
## Region = Western Asia 6 1.4127 [0.6333; 3.1513] 1.0054 1.0027
## Region = Northern Africa 4 0.6103 [0.3597; 1.0357] 0.2912 0.5397
## Region = Southern Africa 1 0.3238 [0.3236; 0.3241] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 990609.81 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 3.0485 [ 3.0476; 3.0494] 1.2 1.5 Northern Africa
## ARGENTINA 1.3337 [ 1.3331; 1.3342] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 9.3000 [ 9.2980; 9.3020] 2.2 1.5 Australia and New Zealand
## AUSTRIA 5.7414 [ 5.7388; 5.7440] 0.5 1.5 Western Europe
## BELARUS 0.1234 [ 0.1230; 0.1238] 0.0 1.5 Eastern Europe
## BELGIUM 5.2455 [ 5.2433; 5.2477] 0.6 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.3756 [ 0.3746; 0.3767] 0.0 1.5 Southern Europe
## BRAZIL 0.5157 [ 0.5155; 0.5158] 1.0 1.5 Central and South America and the Caribbean
## BULGARIA 1.2179 [ 1.2166; 1.2192] 0.1 1.5 Eastern Europe
## CANADA 10.7934 [10.7916; 10.7951] 3.8 1.5 Northern America
## CHILE 0.8795 [ 0.8788; 0.8802] 0.2 1.5 Central and South America and the Caribbean
## CHINA 0.0358 [ 0.0358; 0.0358] 0.5 1.5 Eastern Asia
## COLOMBIA 0.1728 [ 0.1727; 0.1730] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 1.2346 [ 1.2328; 1.2364] 0.0 1.5 Southern Europe
## CZECH REPUBLIC 5.6135 [ 5.6112; 5.6159] 0.6 1.5 Eastern Europe
## ECUADOR 0.5272 [ 0.5266; 0.5277] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 2.0563 [ 2.0558; 2.0568] 1.9 1.5 Northern Africa
## ESTONIA 2.2727 [ 2.2684; 2.2769] 0.0 1.5 Northern Europe
## FINLAND 7.7485 [ 7.7446; 7.7523] 0.4 1.5 Northern Europe
## FRANCE 5.7629 [ 5.7619; 5.7639] 3.6 1.5 Western Europe
## GERMANY 5.3977 [ 5.3969; 5.3985] 4.3 1.5 Western Europe
## GREECE 4.2748 [ 4.2727; 4.2768] 0.4 1.5 Southern Europe
## HUNGARY 2.0198 [ 2.0184; 2.0213] 0.2 1.5 Eastern Europe
## INDIA 0.1090 [ 0.1090; 0.1090] 1.4 1.5 Southern Asia
## IRELAND 10.1551 [10.1504; 10.1599] 0.5 1.5 Northern Europe
## ITALY 2.8330 [ 2.8323; 2.8337] 1.6 1.5 Southern Europe
## JAPAN 3.8160 [ 3.8155; 3.8166] 4.7 1.5 Eastern Asia
## JORDAN 1.1300 [ 1.1289; 1.1311] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.0892 [ 0.0890; 0.0894] 0.0 1.5 Central Asia
## KUWAIT 2.6181 [ 2.6155; 2.6207] 0.1 1.5 Western Asia
## LATVIA 2.1990 [ 2.1955; 2.2024] 0.0 1.5 Northern Europe
## LEBANON 1.4841 [ 1.4826; 1.4856] 0.1 1.5 Western Asia
## LITHUANIA 1.7040 [ 1.7015; 1.7065] 0.0 1.5 Northern Europe
## LUXEMBOURG 4.4402 [ 4.4314; 4.4491] 0.0 1.5 Western Europe
## MEXICO 0.3797 [ 0.3795; 0.3799] 0.5 1.5 Central and South America and the Caribbean
## MOROCCO 0.1463 [ 0.1461; 0.1465] 0.0 1.5 Northern Africa
## NETHERLANDS 3.8860 [ 3.8844; 3.8875] 0.6 1.5 Western Europe
## NEW ZEALAND 3.8784 [ 3.8754; 3.8813] 0.2 1.5 Australia and New Zealand
## NORWAY 6.3251 [ 6.3215; 6.3286] 0.3 1.5 Northern Europe
## PAKISTAN 0.3403 [ 0.3402; 0.3405] 0.7 1.5 Southern Asia
## PERU 0.2237 [ 0.2234; 0.2239] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1306 [ 0.1305; 0.1307] 0.1 1.5 South-eastern Asia
## POLAND 1.2022 [ 1.2016; 1.2028] 0.4 1.5 Eastern Europe
## PORTUGAL 4.9841 [ 4.9818; 4.9863] 0.5 1.5 Southern Europe
## PUERTO RICO 16.0002 [15.9929; 16.0075] 0.5 1.5 Central and South America and the Caribbean
## ROMANIA 1.5396 [ 1.5387; 1.5405] 0.3 1.5 Eastern Europe
## RUSSIA 0.3809 [ 0.3807; 0.3810] 0.5 1.5 Eastern Europe
## SAUDI ARABIA 1.8736 [ 1.8729; 1.8744] 0.6 1.5 Western Asia
## SERBIA 1.4789 [ 1.4775; 1.4802] 0.1 1.5 Southern Europe
## SLOVAKIA 4.7357 [ 4.7327; 4.7387] 0.2 1.5 Eastern Europe
## SLOVENIA 3.8317 [ 3.8273; 3.8361] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.3350 [ 0.3347; 0.3352] 0.2 1.5 Southern Africa
## SOUTH KOREA 2.7612 [ 2.7604; 2.7619] 1.4 1.5 Eastern Asia
## SPAIN 7.1036 [ 7.1024; 7.1049] 3.2 1.5 Southern Europe
## SWEDEN 7.1681 [ 7.1654; 7.1709] 0.7 1.5 Northern Europe
## SWITZERLAND 4.0655 [ 4.0633; 4.0678] 0.3 1.5 Western Europe
## TAIWAN 0.5447 [ 0.5442; 0.5452] 0.1 1.5 Eastern Asia
## THAILAND 0.8093 [ 0.8090; 0.8097] 0.5 1.5 South-eastern Asia
## TUNISIA 0.8396 [ 0.8388; 0.8405] 0.1 1.5 Northern Africa
## TÜRKIYE 5.8298 [ 5.8289; 5.8307] 4.5 1.5 Western Asia
## UNITED ARAB EMIRATES 0.6780 [ 0.6771; 0.6789] 0.1 1.5 Western Asia
## UNITED KINGDOM 13.5562 [13.5548; 13.5577] 8.7 1.5 Northern Europe
## UNITED STATES 13.9471 [13.9465; 13.9478] 43.5 1.5 Northern America
## URUGUAY 1.7113 [ 1.7090; 1.7135] 0.1 1.5 Central and South America and the Caribbean
## VENEZUELA 0.4621 [ 0.4617; 0.4625] 0.1 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 7.0395 [7.0393; 7.0398] 120460.32 0
## Random effects model 1.6148 [1.2059; 2.1623] 3.22 0.0013
##
## Quantifying heterogeneity:
## tau^2 = 1.4424 [1.2156; 3.2813]; tau = 1.2010 [1.1026; 1.8114]
## I^2 = 100.0%; H = 8243.80
##
## Test of heterogeneity:
## Q d.f. p-value
## 4349451471.67 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 13.6626 [13.6620; 13.6632] 8750338.96 100.0%
## Region = Central and South America and t ... 10 0.9997 [ 0.9995; 0.9999] 197578812.20 100.0%
## Region = Northern Europe 8 12.0410 [12.0399; 12.0422] 34578528.47 100.0%
## Region = Eastern Europe 8 1.6558 [ 1.6554; 1.6561] 91869084.78 100.0%
## Region = Southern Europe 8 4.8660 [ 4.8654; 4.8666] 49479430.20 100.0%
## Region = Western Europe 7 5.3659 [ 5.3653; 5.3664] 4314171.43 100.0%
## Region = Australia and New Zealand 2 8.7182 [ 8.7164; 8.7200] 4718592.07 100.0%
## Region = Eastern Asia 4 2.4427 [ 2.4424; 2.4430] 378268970.62 100.0%
## Region = Central Asia 1 0.0892 [ 0.0890; 0.0894] 0.00 --
## Region = South-eastern Asia 2 0.5651 [ 0.5649; 0.5653] 13404327.01 100.0%
## Region = Southern Asia 2 0.1581 [ 0.1581; 0.1581] 22560138.55 100.0%
## Region = Western Asia 6 4.6897 [ 4.6890; 4.6903] 50427376.55 100.0%
## Region = Northern Africa 4 2.2288 [ 2.2284; 2.2292] 22435650.11 100.0%
## Region = Southern Africa 1 0.3350 [ 0.3347; 0.3352] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 3471066050.74 13 0
## Within groups 878385420.93 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 12.2694 [9.5438; 15.7732] 0.0329 0.1813
## Region = Central and South America and t ... 10 0.7538 [0.3087; 1.8404] 2.0743 1.4402
## Region = Northern Europe 8 5.0332 [3.5402; 7.1559] 0.2579 0.5078
## Region = Eastern Europe 8 1.2427 [0.5742; 2.6896] 1.2414 1.1142
## Region = Southern Europe 8 2.4070 [1.6058; 3.6079] 0.3411 0.5841
## Region = Western Europe 7 4.8780 [4.4288; 5.3727] 0.0170 0.1304
## Region = Australia and New Zealand 2 6.0057 [2.5488; 14.1512] 0.3825 0.6184
## Region = Eastern Asia 4 0.6734 [0.1143; 3.9664] 3.2742 1.8095
## Region = Central Asia 1 0.0892 [0.0890; 0.0894] -- --
## Region = South-eastern Asia 2 0.3252 [0.0544; 1.9423] 1.6632 1.2896
## Region = Southern Asia 2 0.1926 [0.0631; 0.5878] 0.6481 0.8050
## Region = Western Asia 6 1.7866 [0.8788; 3.6320] 0.7863 0.8867
## Region = Northern Africa 4 0.9368 [0.5251; 1.6710] 0.3488 0.5906
## Region = Southern Africa 1 0.3350 [0.3347; 0.3352] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 955687.83 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 3
## rate 95%-CI %W(common) %W(random) Region
## ALGERIA 2.9435 [ 2.9427; 2.9444] 1.1 1.5 Northern Africa
## ARGENTINA 1.3902 [ 1.3896; 1.3907] 0.6 1.5 Central and South America and the Caribbean
## AUSTRALIA 9.4384 [ 9.4364; 9.4404] 2.1 1.5 Australia and New Zealand
## AUSTRIA 6.0504 [ 6.0478; 6.0531] 0.5 1.5 Western Europe
## BELARUS 0.1868 [ 0.1863; 0.1872] 0.0 1.5 Eastern Europe
## BELGIUM 5.6263 [ 5.6240; 5.6286] 0.6 1.5 Western Europe
## BOSNIA AND HERZEGOVINA 0.5333 [ 0.5320; 0.5346] 0.0 1.5 Southern Europe
## BRAZIL 0.6318 [ 0.6316; 0.6320] 1.2 1.5 Central and South America and the Caribbean
## BULGARIA 1.3771 [ 1.3757; 1.3785] 0.1 1.5 Eastern Europe
## CANADA 10.8953 [10.8936; 10.8971] 3.7 1.5 Northern America
## CHILE 0.9280 [ 0.9273; 0.9288] 0.2 1.5 Central and South America and the Caribbean
## CHINA 0.0438 [ 0.0438; 0.0438] 0.6 1.5 Eastern Asia
## COLOMBIA 0.1955 [ 0.1953; 0.1957] 0.1 1.5 Central and South America and the Caribbean
## CROATIA 1.3816 [ 1.3797; 1.3834] 0.1 1.5 Southern Europe
## CZECH REPUBLIC 6.0546 [ 6.0522; 6.0571] 0.6 1.5 Eastern Europe
## ECUADOR 0.5475 [ 0.5469; 0.5481] 0.1 1.5 Central and South America and the Caribbean
## EGYPT 3.9344 [ 3.9338; 3.9351] 3.5 1.5 Northern Africa
## ESTONIA 3.0000 [ 2.9952; 3.0049] 0.0 1.5 Northern Europe
## FINLAND 8.2826 [ 8.2786; 8.2865] 0.4 1.5 Northern Europe
## FRANCE 6.0179 [ 6.0169; 6.0188] 3.5 1.5 Western Europe
## GERMANY 5.6636 [ 5.6628; 5.6645] 4.3 1.5 Western Europe
## GREECE 4.6340 [ 4.6318; 4.6361] 0.4 1.5 Southern Europe
## HUNGARY 2.1507 [ 2.1492; 2.1522] 0.2 1.5 Eastern Europe
## INDIA 0.1168 [ 0.1168; 0.1168] 1.4 1.5 Southern Asia
## IRELAND 8.5190 [ 8.5147; 8.5234] 0.4 1.5 Northern Europe
## ITALY 2.8818 [ 2.8811; 2.8825] 1.6 1.5 Southern Europe
## JAPAN 4.0686 [ 4.0680; 4.0692] 4.7 1.5 Eastern Asia
## JORDAN 0.6513 [ 0.6504; 0.6521] 0.1 1.5 Western Asia
## KAZAKHSTAN 0.1047 [ 0.1044; 0.1049] 0.0 1.5 Central Asia
## KUWAIT 3.2250 [ 3.2222; 3.2279] 0.1 1.5 Western Asia
## LATVIA 2.6394 [ 2.6357; 2.6432] 0.0 1.5 Northern Europe
## LEBANON 1.5687 [ 1.5672; 1.5703] 0.1 1.5 Western Asia
## LITHUANIA 2.0118 [ 2.0091; 2.0146] 0.1 1.5 Northern Europe
## LUXEMBOURG 4.4238 [ 4.4151; 4.4326] 0.0 1.5 Western Europe
## MEXICO 0.4124 [ 0.4122; 0.4126] 0.5 1.5 Central and South America and the Caribbean
## MOROCCO 0.2011 [ 0.2009; 0.2014] 0.1 1.5 Northern Africa
## NETHERLANDS 4.0538 [ 4.0522; 4.0554] 0.6 1.5 Western Europe
## NEW ZEALAND 5.0261 [ 5.0228; 5.0295] 0.2 1.5 Australia and New Zealand
## NORWAY 6.6320 [ 6.6284; 6.6356] 0.3 1.5 Northern Europe
## PAKISTAN 0.3985 [ 0.3984; 0.3986] 0.8 1.5 Southern Asia
## PERU 0.2562 [ 0.2559; 0.2565] 0.1 1.5 Central and South America and the Caribbean
## PHILIPPINES 0.1572 [ 0.1570; 0.1573] 0.2 1.5 South-eastern Asia
## POLAND 1.6797 [ 1.6790; 1.6803] 0.6 1.5 Eastern Europe
## PORTUGAL 5.1522 [ 5.1499; 5.1545] 0.5 1.5 Southern Europe
## PUERTO RICO 15.1233 [15.1160; 15.1305] 0.4 1.5 Central and South America and the Caribbean
## ROMANIA 1.6365 [ 1.6356; 1.6375] 0.3 1.5 Eastern Europe
## RUSSIA 0.5050 [ 0.5048; 0.5052] 0.7 1.5 Eastern Europe
## SAUDI ARABIA 1.1357 [ 1.1351; 1.1363] 0.3 1.5 Western Asia
## SERBIA 1.9559 [ 1.9543; 1.9574] 0.2 1.5 Southern Europe
## SLOVAKIA 5.1626 [ 5.1594; 5.1657] 0.3 1.5 Eastern Europe
## SLOVENIA 3.9999 [ 3.9954; 4.0044] 0.1 1.5 Southern Europe
## SOUTH AFRICA 0.3738 [ 0.3736; 0.3741] 0.2 1.5 Southern Africa
## SOUTH KOREA 3.0995 [ 3.0987; 3.1003] 1.4 1.5 Eastern Asia
## SPAIN 7.3282 [ 7.3269; 7.3295] 3.1 1.5 Southern Europe
## SWEDEN 7.6667 [ 7.6638; 7.6695] 0.7 1.5 Northern Europe
## SWITZERLAND 4.2259 [ 4.2237; 4.2282] 0.3 1.5 Western Europe
## TAIWAN 0.6414 [ 0.6408; 0.6419] 0.1 1.5 Eastern Asia
## THAILAND 0.9387 [ 0.9383; 0.9390] 0.6 1.5 South-eastern Asia
## TUNISIA 2.0328 [ 2.0314; 2.0342] 0.2 1.5 Northern Africa
## TÜRKIYE 6.0922 [ 6.0913; 6.0931] 4.5 1.5 Western Asia
## UNITED ARAB EMIRATES 0.6640 [ 0.6631; 0.6648] 0.1 1.5 Western Asia
## UNITED KINGDOM 13.8879 [13.8864; 13.8894] 8.4 1.5 Northern Europe
## UNITED STATES 14.2069 [14.2062; 14.2076] 42.0 1.5 Northern America
## URUGUAY 1.5402 [ 1.5380; 1.5424] 0.0 1.5 Central and South America and the Caribbean
## VENEZUELA 0.4182 [ 0.4178; 0.4186] 0.1 1.5 Central and South America and the Caribbean
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 7.0838 [7.0836; 7.0840] 124461.49 0
## Random effects model 1.7840 [1.3391; 2.3767] 3.95 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.3924 [1.1595; 3.1026]; tau = 1.1800 [1.0768; 1.7614]
## I^2 = 100.0%; H = 8406.45
##
## Test of heterogeneity:
## Q d.f. p-value
## 4522777067.76 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## Region = Northern America 2 13.9085 [13.9079; 13.9092] 9560537.35 100.0%
## Region = Central and South America and t ... 10 0.9891 [ 0.9890; 0.9893] 171398138.17 100.0%
## Region = Northern Europe 8 12.2829 [12.2818; 12.2841] 34865979.22 100.0%
## Region = Eastern Europe 8 1.8254 [ 1.8251; 1.8258] 93547042.79 100.0%
## Region = Southern Europe 8 5.0234 [ 5.0228; 5.0241] 50160342.70 100.0%
## Region = Western Europe 7 5.6248 [ 5.6243; 5.6254] 4606409.40 100.0%
## Region = Australia and New Zealand 2 8.9062 [ 8.9045; 8.9080] 3139085.02 100.0%
## Region = Eastern Asia 4 2.5423 [ 2.5420; 2.5426] 431204723.26 100.0%
## Region = Central Asia 1 0.1047 [ 0.1044; 0.1049] 0.00 --
## Region = South-eastern Asia 2 0.6512 [ 0.6510; 0.6515] 15554089.24 100.0%
## Region = Southern Asia 2 0.1792 [ 0.1791; 0.1792] 30302374.24 100.0%
## Region = Western Asia 6 4.9819 [ 4.9812; 4.9826] 63449402.43 100.0%
## Region = Northern Africa 4 3.4380 [ 3.4375; 3.4385] 27366592.32 100.0%
## Region = Southern Africa 1 0.3738 [ 0.3736; 0.3741] 0.00 --
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 3587622351.60 13 0
## Within groups 935154716.15 51 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## Region = Northern America 2 12.4414 [9.5922; 16.1369] 0.0352 0.1877
## Region = Central and South America and t ... 10 0.7858 [0.3513; 1.7577] 1.6869 1.2988
## Region = Northern Europe 8 5.4634 [3.8557; 7.7413] 0.2529 0.5029
## Region = Eastern Europe 8 1.4878 [0.7272; 3.0440] 1.0673 1.0331
## Region = Southern Europe 8 2.7097 [1.8201; 4.0340] 0.3298 0.5743
## Region = Western Europe 7 5.0852 [4.6139; 5.6045] 0.0172 0.1313
## Region = Australia and New Zealand 2 6.8876 [3.7143; 12.7719] 0.1985 0.4456
## Region = Eastern Asia 4 0.7714 [0.1307; 4.5532] 3.2821 1.8117
## Region = Central Asia 1 0.1047 [0.1044; 0.1049] -- --
## Region = South-eastern Asia 2 0.3841 [0.0666; 2.2134] 1.5971 1.2638
## Region = Southern Asia 2 0.2157 [0.0648; 0.7182] 0.7530 0.8678
## Region = Western Asia 6 1.5728 [0.6399; 3.8658] 1.2632 1.1239
## Region = Northern Africa 4 1.4751 [0.8494; 2.5618] 0.3173 0.5633
## Region = Southern Africa 1 0.3738 [0.3736; 0.3741] -- --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1046742.17 13 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
#Multinational
gaba_global2<-as.data.frame(do.call(rbind, datout))[c(5:9)]
gaba_global2$Year<-as.numeric(gaba_global2$Year)
gaba_global2$`DDD/TID`<-as.numeric(gaba_global2$`DDD/TID`)
gaba_global2$`DDD/TID - lower`<-as.numeric(gaba_global2$`DDD/TID - lower`)
gaba_global2$`DDD/TID - upper`<-as.numeric(gaba_global2$`DDD/TID - upper`)
gaba_global2$Area<-"Multinational"
gaba_global2$Drug<-c(rep(("Gabapentin"), 11),
rep(("Pregabalin"), 11),
rep(("Gabapentinoids"), 11))
gaba_global3<-subset(gaba_global2,Drug!="MIROGABALIN")
a<-ggplot(gaba_global3, aes(x = Year, y = `DDD/TID`*10, group=Drug, colour=Drug, fill=Drug))+
geom_line() +
geom_ribbon(aes(ymin = `DDD/TID - lower`*10, ymax = `DDD/TID - upper`*10), alpha = 0.1, colour = NA) +
scale_x_continuous(breaks = c(2008:2018))+
ggtitle("Pooled multinational gabapentinoids consumption over time")+
ylab("Defined daily dose per 10000 inhabitants per day")+
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
a
##Regional
gaba_regional<-as.data.frame(do.call(rbind, datout))[c(14:18)]
gaba_regional$Year<-as.numeric(gaba_regional$Year)
out <- unlist(gaba_regional)
Year<-out[1:33]
Drug<-out[34:66]
DDDTID<-out[67:528]
lower<-out[529:990]
upper<-out[991:1452]
out2<-data.frame(Year,Drug)
out3<-do.call("rbind", replicate(14, out2, simplify = FALSE))
newdata <- out3%>% arrange(Drug, Year)
newdata$DDDTID<-DDDTID
newdata$lower<-lower
newdata$upper<-upper
region_name<-(rep(c("Northern America","Central and South America and the Caribbean",
"Northern Europe", "Eastern Europe","Southern Europe", "Western Europe",
"Australia and New Zealand" ,
"Eastern Asia" , "Central Asia",
"South-eastern Asia","Southern Asia" ,
"Western Asia",
"Northern Africa","Southern Africa"),33))
newdata$Area<-region_name
newdata$Drug<-c(rep(("Gabapentin"), 154),
rep(("Pregabalin"), 154),
rep(("Gabapentinoids"), 154))
gaba_regional2<-newdata
gaba_regional2$`DDD/TID`<-gaba_regional2$DDDTID
gaba_regional2$`DDD/TID - lower`<-gaba_regional2$lower
gaba_regional2$`DDD/TID - upper`<-gaba_regional2$upper
gaba_regional3<-gaba_regional2[c(1,2,6:9)]
pool_Global_region<-rbind(gaba_global3,gaba_regional3)
pool_Global_region$Drug<- factor(pool_Global_region$Drug, levels = c("Gabapentinoids","Gabapentin","Gabapentin Enacarbil","Pregabalin"))
pool_Global_region$Area<-factor(pool_Global_region$Area,levels=c("Multinational","Northern America","Central and South America and the Caribbean",
"Northern Europe", "Eastern Europe","Southern Europe", "Western Europe",
"Australia and New Zealand" ,
"Eastern Asia" , "Central Asia",
"South-eastern Asia","Southern Asia" ,
"Western Asia",
"Northern Africa","Southern Africa"))
pool_Global_region$Year<-as.numeric(pool_Global_region$Year)
pool_Global_region[is.na(pool_Global_region)]<-0
library(ggbreak)
a<-ggplot(pool_Global_region, aes(x = Year, y = `DDD/TID`, group=Drug, colour=Drug, fill=Drug))+
geom_line() +
facet_wrap(.~Area,nrow=1)+
geom_ribbon(aes(ymin = `DDD/TID - lower`, ymax = `DDD/TID - upper`), alpha = 0.1, colour = NA) +
scale_y_break(c(20,62),scales = 0.1)+
theme_classic()+
scale_x_continuous(breaks = c(2008:2018))+
theme(panel.border=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background=element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.line=element_line(),axis.ticks.x = element_blank())+
ggtitle("Pooled multinational gabapentinoids consumption over time")
a
write.csv(pool_Global_region,"D:/R/midas gaba/R1/Output_13_Sensitivity_3_Pooled_Gaba_consumption_CI.csv")
library(DT)
datatable(pool_Global_region[c(6,1:5)], options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Average annual percentage change
Data entries with DDD=0 were removed as they do not contribute to average annual percentage change where mesaures changes between study years.
Data view - showing which zeros were removed
National level
options(width = 30)
library(plyr)
library(Hmisc)
library(DT)
library(tibble)
lm.cty_gaba_2<-subset(lm.cty_gaba, DDD>0)
models <- dlply(lm.cty_gaba_2, .(Country,Drug), function(df)
lm(log(DDDPTPD) ~ Year, data = df))
coef<-sapply(models, function(df) summary(df)$coefficients[2])
lm.results<-data.frame(coef)
ad_extract_ci <- function(x){
temp_lw <- confint.lm(x)[2,1]
temp_up <- confint.lm(x)[2,2]
return(c(temp_lw,temp_up))
}
lower<-unlist(lapply(lapply(models,ad_extract_ci),"[",1))
lm.results<-cbind(lm.results, lower)
upper<-unlist(lapply(lapply(models,ad_extract_ci),"[",2))
lm.results<-cbind(lm.results, upper)
pvalue<-sapply(models, function(df) summary(df)$coefficients[8])
lm.results<-cbind(lm.results, pvalue)
lm.results<-rownames_to_column(lm.results)
colnames(lm.results)[which(names(lm.results) == "rowname")] <- "Country"
lm.results$expcoef<-(exp(lm.results$coef)-1)*100
lm.results$explower<-(exp(lm.results$lower)-1)*100
lm.results$expupper<-(exp(lm.results$upper)-1)*100
lm.results2<-(lm.results) %>% separate(Country, c("Country", "Drug"), sep="[.]")
cty.lm.results<-distinct(left_join(lm.results2,pool.new_set.2[c(2,3,7)]))
Global
library(plyr)
library(lme4)
library(nlme)
#
# mixed_results_Global<-data.frame()
# lm.cty_gaba_x<-split(lm.cty_gaba_2, list(lm.cty_gaba_2$Drug))
# for (i in 1:4){
# Global_model_1 <- lme(log(DDDPTPD)~Year, random = ~1|Country,
# correlation = corAR1(form = ~ Year|Country), data = as.data.frame(lm.cty_gaba_x[i], col.names = c("")))
# summary(Global_model_1)
# mixed_est<-intervals(Global_model_1, level = 0.95, which = "fixed")
# mixed_est_2 <- data.frame(mixed_est$fixed)
# mixed_pval<-anova(Global_model_1)
# mixed_pval<-as.data.frame(mixed_pval)
# mixed_results_1<-cbind(mixed_est_2,mixed_pval)
# mixed_results_1$Model<-"Global_country_int_wAR1"
# mixed_results_1$expcoef<-(exp(mixed_results_1$est.)-1)*100
# mixed_results_1$explower<-(exp(mixed_results_1$lower)-1)*100
# mixed_results_1$expupper<-(exp(mixed_results_1$upper)-1)*100
#
# mixglo<-rbind(mixed_results_1)
# mixglo$Drug<-c(unique(lm.cty_gaba$Drug)[i])
# mixed_results_Global<-rbind(mixed_results_Global,mixglo)
# }
#
# datatable(mixed_results_Global[c(8:12,7,1:6)], options = list(
# autoWidth = TRUE,
# columnDefs = list(list(width = '100px', targets = c(1, 3)))
# ))
asd<-subset(lm.cty_gaba_2, Drug=="Gabapentinoids")
Global_model_1 <- lme(log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(form = ~ Year|Country), data = asd)
mixed_est<-intervals(Global_model_1, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(Global_model_1)
mixed_pval<-as.data.frame(mixed_pval)
mixed_results_1<-cbind(mixed_est_2,mixed_pval)
mixed_results_1$Drug<-"Gabapentinoids"
mixed_results_1$expcoef<-(exp(mixed_results_1$est.)-1)*100
mixed_results_1$explower<-(exp(mixed_results_1$lower)-1)*100
mixed_results_1$expupper<-(exp(mixed_results_1$upper)-1)*100
asd<-subset(lm.cty_gaba_2, Drug=="Gabapentin")
Global_model_1 <- lme(log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(form = ~ Year|Country), data = asd)
mixed_est<-intervals(Global_model_1, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(Global_model_1)
mixed_pval<-as.data.frame(mixed_pval)
mixed_results_2<-cbind(mixed_est_2,mixed_pval)
mixed_results_2$Drug<-"Gabapentin"
mixed_results_2$expcoef<-(exp(mixed_results_2$est.)-1)*100
mixed_results_2$explower<-(exp(mixed_results_2$lower)-1)*100
mixed_results_2$expupper<-(exp(mixed_results_2$upper)-1)*100
asd<-subset(lm.cty_gaba_2, Drug=="Pregabalin")
Global_model_1 <- lme(log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(form = ~ Year|Country), data = asd)
mixed_est<-intervals(Global_model_1, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(Global_model_1)
mixed_pval<-as.data.frame(mixed_pval)
mixed_results_3<-cbind(mixed_est_2,mixed_pval)
mixed_results_3$Drug<-"Pregabalin"
mixed_results_3$expcoef<-(exp(mixed_results_3$est.)-1)*100
mixed_results_3$explower<-(exp(mixed_results_3$lower)-1)*100
mixed_results_3$expupper<-(exp(mixed_results_3$upper)-1)*100
mixed_results_Global<-rbind(mixed_results_1,mixed_results_2,mixed_results_3,mixed_results_4)
datatable(mixed_results_Global, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Regional
Gabapentinoids
library(DT)
library(data.table)
regionalmlm<-subset(lm.cty_gaba_2,Drug=="Gabapentinoids")
my_update_function <- function(x){
regionalmlm2<-regionalmlm[regionalmlm$Region==x,]
regional_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = regionalmlm2)
mixed_est<-intervals(regional_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(regional_lme)
mixed_results_regional<-cbind(mixed_est_2,mixed_pval)
mixed_results_regional$Model<-x
mixed_results_regional$expcoef<-(exp(mixed_results_regional$est.)-1)*100
mixed_results_regional$explower<-(exp(mixed_results_regional$lower)-1)*100
mixed_results_regional$expupper<-(exp(mixed_results_regional$upper)-1)*100
mixed_results_regional <- mixed_results_regional[2,]
return(mixed_results_regional)
}
lapply(unique(lm.cty_gaba_2$Region),my_update_function)
gabapentinoids.mlm_region<-rbindlist(lapply(unique(lm.cty_gaba_2$Region),
my_update_function))
gabapentinoids.mlm_region<-as.data.frame(gabapentinoids.mlm_region)
gabapentinoids.mlm_region$Drug<-"Gabapentinoids"
Gabapentin
library(DT)
library(data.table)
regionalmlm<-subset(lm.cty_gaba_2,Drug=="Gabapentin")
my_update_function <- function(x){
regionalmlm2<-regionalmlm[regionalmlm$Region==x,]
regional_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = regionalmlm2)
mixed_est<-intervals(regional_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(regional_lme)
mixed_results_regional<-cbind(mixed_est_2,mixed_pval)
mixed_results_regional$Model<-x
mixed_results_regional$expcoef<-(exp(mixed_results_regional$est.)-1)*100
mixed_results_regional$explower<-(exp(mixed_results_regional$lower)-1)*100
mixed_results_regional$expupper<-(exp(mixed_results_regional$upper)-1)*100
mixed_results_regional <- mixed_results_regional[2,]
return(mixed_results_regional)
}
lapply(unique(regionalmlm$Region),my_update_function)
gabapentin.mlm_region<-rbindlist(lapply(unique(regionalmlm$Region),
my_update_function))
gabapentin.mlm_region<-as.data.frame(gabapentin.mlm_region)
gabapentin.mlm_region$Drug<-"Gabapentin"
Pregabalin
library(DT)
library(data.table)
regionalmlm<-subset(lm.cty_gaba_2,Drug=="Pregabalin")
my_update_function <- function(x){
regionalmlm2<-regionalmlm[regionalmlm$Region==x,]
regional_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = regionalmlm2)
mixed_est<-intervals(regional_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(regional_lme)
mixed_results_regional<-cbind(mixed_est_2,mixed_pval)
mixed_results_regional$Model<-x
mixed_results_regional$expcoef<-(exp(mixed_results_regional$est.)-1)*100
mixed_results_regional$explower<-(exp(mixed_results_regional$lower)-1)*100
mixed_results_regional$expupper<-(exp(mixed_results_regional$upper)-1)*100
mixed_results_regional <- mixed_results_regional[2,]
return(mixed_results_regional)
}
lapply(unique(regionalmlm$Region),my_update_function)
pregabalin.mlm_region<-rbindlist(lapply(unique(regionalmlm$Region),
my_update_function))
pregabalin.mlm_region<-as.data.frame(pregabalin.mlm_region)
pregabalin.mlm_region$Drug<-"Pregabalin"
region_reg_mlm<-rbind(gabapentinoids.mlm_region,gabapentin.mlm_region, enacarbil.mlm_region, pregabalin.mlm_region)
Result table
bind_cty_lm<-cty.lm.results[c(2,10,1,7:9,6)]
names(bind_cty_lm)[7]<-"p-value"
bind_reg_lm<-region_reg_mlm
bind_reg_lm$Country<-bind_reg_lm$Model
bind_reg_lm$Region<-bind_reg_lm$Model
bind_reg_lm<-bind_reg_lm[c(12,14,13,9:11,7)]
bind_glo_lm<-mixed_results_Global[c(2,4,6,8),]
bind_glo_lm<-bind_glo_lm[c(8:11,7)]
bind_glo_lm$Country<-"Multinational"
bind_glo_lm$Region<-"Multinational"
bind_glo_lm<-bind_glo_lm[c(7,6,1:5)]
bind_lm<-rbind(bind_cty_lm,bind_reg_lm,bind_glo_lm)
write.csv(bind_lm,"D:/R/midas gaba/R1/Output_14_Sensitivity_3_lm_cty_region_multi.csv")
datatable(bind_lm, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Subgroup: country income level
Gabapentinoids
library(readxl)
GDP <- read_excel("D:/R/midas gaba/Income.xlsx")
'%ni%' <- Negate('%in%')
GDP$country <- toupper(GDP$country)
rename <- lm.cty_gaba%>%filter(Country %ni% GDP$country)
rename<-unique(rename$Country)
names(GDP)[names(GDP) == 'country'] <- "Country"
GDP[GDP$Country == "KOREA, REP.", "Country"] <- "SOUTH KOREA"
GDP[GDP$Country == "EGYPT, ARAB REP.", "Country"] <- "EGYPT"
GDP[GDP$Country == "RUSSIAN FEDERATION", "Country"] <- "RUSSIA"
GDP[GDP$Country == "SLOVAK REPUBLIC", "Country"] <- "SLOVAKIA"
GDP[GDP$Country == "VENEZUEL", "Country"] <- "VENEZUELA"
GDP[GDP$Country == "TURKEY", "Country"] <- "TÜRKİYE"
lm.cty_gaba$Year<-as.numeric(lm.cty_gaba$Year)
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Gabapentinoids")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
## [[1]]
## lower est.
## Year 0.1653786 0.1986655
## upper numDF denDF
## Year 0.2319524 1 198
## F-value p-value Model
## Year 138.5224 0 UMIC
## AAPC explower
## Year 21.97739 17.98397
## expupper
## Year 26.10597
##
## [[2]]
## lower est.
## Year 0.1148998 0.1295971
## upper numDF denDF
## Year 0.1442943 1 376
## F-value p-value Model
## Year 300.6182 0 HIC
## AAPC explower
## Year 13.83696 12.1761
## expupper
## Year 15.5224
##
## [[3]]
## lower est.
## Year 0.1566216 0.1972621
## upper numDF denDF
## Year 0.2379026 1 59
## F-value
## Year 94.33242
## p-value
## Year 0.00000000000007582823
## Model AAPC explower
## Year LMIC 21.80633 16.95529
## expupper
## Year 26.85856
gabapentinoids.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
gabapentinoids.mlm_income$Drug<-"Gabapentinoids"
datatable(as.data.frame(gabapentinoids.mlm_income[,c(8:11,7)]),caption = "Average annual percentage change by income level", options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
Gabapentin
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Gabapentin")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
gabapentin.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
gabapentin.mlm_income<-as.data.frame(gabapentin.mlm_income)
gabapentin.mlm_income$Drug<-"Gabapentin"
Pregabalin
lm.cty_gaba_3<-subset(lm.cty_gaba_2,Drug=="Pregabalin")
gdp.2 <- left_join(lm.cty_gaba_3,GDP,by = c("Country") )
library(plyr)
library(lme4)
library(nlme)
library(DT)
my_update_function <- function(x){
incomemlm<-gdp.2[gdp.2$income==x,]
income_lme <- lme(fixed = log(DDDPTPD)~Year, random = ~1|Country,
correlation = corAR1(), data = incomemlm,
control =list(opt = "optim",msMaxIter = 1000, msMaxEval = 1000, tolerance = 1e-200))
mixed_est<-intervals(income_lme, level = 0.95, which = "fixed")
mixed_est_2 <- data.frame(mixed_est$fixed)
mixed_pval<-anova(income_lme)
mixed_results_income<-cbind(mixed_est_2,mixed_pval)
mixed_results_income$Model<-x
mixed_results_income$AAPC<-(exp(mixed_results_income$est.)-1)*100
mixed_results_income$explower<-(exp(mixed_results_income$lower)-1)*100
mixed_results_income$expupper<-(exp(mixed_results_income$upper)-1)*100
mixed_results_income <- mixed_results_income[2,]
return(mixed_results_income)
}
lapply(unique(gdp.2$income),my_update_function)
pregabalin.mlm_income<-rbindlist(lapply(unique(gdp.2$income),
my_update_function))
pregabalin.mlm_income$Drug<-"Pregabalin"
pregabalin.mlm_income<-as.data.frame(pregabalin.mlm_income)
Individual drugs income AAPC
drug_income_mlm<-rbind(gabapentinoids.mlm_income,gabapentin.mlm_income, pregabalin.mlm_income)
datatable(as.data.frame(drug_income_mlm[,c(12,8:11,7)]),caption = "Average annual percentage change by income level", options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
write.csv(drug_income_mlm,"D:/R/midas gaba/R1/Output_15_Sensitivity_3_Subgroup_AAPC_income.csv")
Meta-analysis of DDD/TID by Year
When DDD=0 or extremely small, Country data wont be counted as number of studies in the meta analysis
options(width=800)
library(Rcpp)
library(meta)
library(data.table)
library(dplyr)
set_subzero<- left_join(analy,GDP,by = c("Country") )
set_subzero$DDD_dum=set_subzero$DDD
set_subzero$DDD_dum[set_subzero$DDD==0]<-0.00001
CIs<-pois.approx(set_subzero$DDD_dum, set_subzero$Population*365.25, conf.level = 0.95)
meta.gaba<-cbind(set_subzero,CIs)
meta.gaba$income <- factor(meta.gaba$income, levels =
c("HIC","UMIC","LMIC"))
meta <- function(rho, iseed){
meta.gaba_1<- subset(meta.gaba, Year==rho & Drug==iseed)
m1_var<-metagen(log(meta.gaba_1$rate),
lower = log(meta.gaba_1$lower),
upper = log(meta.gaba_1$upper),
studlab = meta.gaba_1$Country,
sm = "IRLN", method.tau = "DL",
comb.fixed = TRUE,
byvar = meta.gaba_1$income)
print(c(rho, iseed))
print(summary(m1_var), digits=4)
est.by.random<-c("Year", "DDD/TID", "DDD/TID - lower","DDD/TID - upper")
est.by.random$Year<-rho
est.by.random$Drug<-iseed
est.by.random$`DDD/TID`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$TE)))))
est.by.random$`DDD/TID - lower`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$lower)))))
est.by.random$`DDD/TID - upper`<-(t(data.frame(as.list(exp((summary(m1_var))$within.random$upper)))))
est.by.random$income<-(t(data.frame(as.list(((summary(m1_var))$byvar)))))
return(c(est.by.random))
}
datin <- expand.grid(rho = unique(meta.gaba$Year), iseed = unique(meta.gaba$Drug))
i <- 1:nrow(datin)
datout <- with(datin,
lapply(i, function(j){meta(rho[j], iseed[j])}))
## [1] 2008 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1047 [0.1045; 0.1049] 0.2 1.7 UMIC
## AUSTRALIA 0.7587 [0.7581; 0.7593] 0.7 1.7 HIC
## AUSTRIA 1.4440 [1.4427; 1.4454] 0.6 1.7 HIC
## BELARUS 0.0049 [0.0048; 0.0049] 0.0 1.7 UMIC
## BELGIUM 0.3490 [0.3484; 0.3495] 0.2 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0522 [0.0522; 0.0523] 0.5 1.7 UMIC
## BULGARIA 0.0867 [0.0863; 0.0870] 0.0 1.7 UMIC
## CANADA 2.4878 [2.4870; 2.4887] 3.8 1.7 HIC
## CHILE 0.0425 [0.0423; 0.0426] 0.0 1.7 HIC
## CHINA 0.0009 [0.0009; 0.0009] 0.1 1.7 UMIC
## COLOMBIA 0.0340 [0.0339; 0.0341] 0.1 1.7 UMIC
## CROATIA 0.2926 [0.2918; 0.2935] 0.1 1.7 HIC
## CZECH REPUBLIC 0.7153 [0.7145; 0.7162] 0.3 1.7 HIC
## ECUADOR 0.0727 [0.0725; 0.0730] 0.0 1.7 UMIC
## EGYPT 0.0759 [0.0758; 0.0760] 0.3 1.7 LMIC
## ESTONIA 0.0754 [0.0746; 0.0761] 0.0 1.7 HIC
## FINLAND 0.9593 [0.9579; 0.9607] 0.2 1.7 HIC
## FRANCE 1.3793 [1.3788; 1.3798] 3.9 1.7 HIC
## GERMANY 1.2294 [1.2290; 1.2298] 4.6 1.7 HIC
## GREECE 0.8335 [0.8326; 0.8344] 0.4 1.7 HIC
## HUNGARY 0.2259 [0.2254; 0.2264] 0.1 1.7 HIC
## INDIA 0.0096 [0.0096; 0.0096] 0.5 1.7 LMIC
## IRELAND 0.9193 [0.9178; 0.9207] 0.2 1.7 HIC
## ITALY 0.5709 [0.5706; 0.5712] 1.5 1.7 HIC
## JAPAN 0.1016 [0.1015; 0.1017] 0.6 1.7 HIC
## JORDAN 0.0755 [0.0751; 0.0758] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0298 [0.0294; 0.0301] 0.0 1.7 HIC
## LATVIA 0.3321 [0.3308; 0.3334] 0.0 1.7 HIC
## LEBANON 0.2416 [0.2409; 0.2424] 0.1 1.7 UMIC
## LITHUANIA 0.2187 [0.2179; 0.2195] 0.0 1.7 HIC
## LUXEMBOURG 0.8551 [0.8508; 0.8595] 0.0 1.7 HIC
## MEXICO 0.0713 [0.0712; 0.0713] 0.4 1.7 UMIC
## MOROCCO 0.0074 [0.0073; 0.0074] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.9749 [0.9734; 0.9765] 0.2 1.7 HIC
## NORWAY 1.1055 [1.1040; 1.1071] 0.2 1.7 HIC
## PAKISTAN 0.0542 [0.0542; 0.0543] 0.4 1.7 LMIC
## PERU 0.0242 [0.0241; 0.0243] 0.0 1.7 UMIC
## PHILIPPINES 0.0260 [0.0260; 0.0261] 0.1 1.7 LMIC
## POLAND 0.1470 [0.1468; 0.1472] 0.3 1.7 HIC
## PORTUGAL 1.3098 [1.3087; 1.3109] 0.6 1.7 HIC
## PUERTO RICO 3.7459 [3.7426; 3.7492] 0.6 1.7 HIC
## ROMANIA 0.0597 [0.0595; 0.0599] 0.1 1.7 UMIC
## RUSSIA 0.0152 [0.0152; 0.0152] 0.1 1.7 UMIC
## SAUDI ARABIA 0.1059 [0.1057; 0.1061] 0.1 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.5982 [0.5971; 0.5993] 0.1 1.7 HIC
## SLOVENIA 0.4548 [0.4533; 0.4564] 0.0 1.7 HIC
## SOUTH AFRICA 0.0515 [0.0514; 0.0516] 0.1 1.7 UMIC
## SOUTH KOREA 0.6427 [0.6424; 0.6431] 1.4 1.7 HIC
## SPAIN 1.7859 [1.7853; 1.7866] 3.8 1.7 HIC
## SWEDEN 1.2074 [1.2063; 1.2086] 0.5 1.7 HIC
## SWITZERLAND 0.6300 [0.6291; 0.6309] 0.2 1.7 HIC
## TAIWAN 0.1750 [0.1747; 0.1753] 0.2 1.7 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0646 [0.0643; 0.0648] 0.0 1.7 LMIC
## TÜRKIYE 1.1817 [1.1813; 1.1821] 3.8 1.7 UMIC
## UNITED ARAB EMIRATES 0.1001 [0.0997; 0.1005] 0.0 1.7 HIC
## UNITED KINGDOM 1.6870 [1.6864; 1.6875] 4.8 1.7 HIC
## UNITED STATES 4.4760 [4.4756; 4.4764] 62.1 1.7 HIC
## URUGUAY 0.1957 [0.1949; 0.1964] 0.0 1.7 HIC
## VENEZUELA 0.4496 [0.4492; 0.4500] 0.6 1.7 UMIC
##
## Number of studies combined: k = 59
##
## rate 95%-CI z p-value
## Common effect model 2.4562 [2.4561; 2.4564] 25402.37 0
## Random effects model 0.2042 [0.1450; 0.2876] -9.10 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.7993 [1.6079; 5.2238]; tau = 1.3414 [1.2680; 2.2856]
## I^2 = 100.0%; H = 3867.50
##
## Test of heterogeneity:
## Q d.f. p-value
## 867537160.53 58 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 2.9227 [2.9225; 2.9229] 383220712.12 100.0%
## income = UMIC 16 0.4642 [0.4641; 0.4644] 100212177.30 100.0%
## income = LMIC 6 0.0279 [0.0279; 0.0279] 8846212.59 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 375258058.52 2 0
## Within groups 492279102.01 56 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.5040 [0.3676; 0.6909] 0.9588 0.9792
## income = UMIC 16 0.0532 [0.0208; 0.1360] 3.6641 1.9142
## income = LMIC 6 0.0281 [0.0120; 0.0657] 1.1296 1.0628
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 53.26 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1194 [0.1193; 0.1196] 0.2 1.7 UMIC
## AUSTRALIA 0.7770 [0.7764; 0.7776] 0.7 1.7 HIC
## AUSTRIA 1.5762 [1.5748; 1.5776] 0.5 1.7 HIC
## BELARUS 0.0093 [0.0092; 0.0094] 0.0 1.7 UMIC
## BELGIUM 0.3669 [0.3663; 0.3675] 0.2 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0590 [0.0590; 0.0591] 0.5 1.7 UMIC
## BULGARIA 0.1204 [0.1200; 0.1208] 0.0 1.7 UMIC
## CANADA 2.6364 [2.6355; 2.6373] 3.6 1.7 HIC
## CHILE 0.0335 [0.0333; 0.0336] 0.0 1.7 HIC
## CHINA 0.0020 [0.0020; 0.0020] 0.1 1.7 UMIC
## COLOMBIA 0.0287 [0.0286; 0.0288] 0.1 1.7 UMIC
## CROATIA 0.2979 [0.2971; 0.2988] 0.1 1.7 HIC
## CZECH REPUBLIC 0.8909 [0.8900; 0.8918] 0.4 1.7 HIC
## ECUADOR 0.0696 [0.0693; 0.0698] 0.0 1.7 UMIC
## EGYPT 0.0833 [0.0832; 0.0834] 0.3 1.7 LMIC
## ESTONIA 0.1091 [0.1081; 0.1100] 0.0 1.7 HIC
## FINLAND 0.9637 [0.9623; 0.9650] 0.2 1.7 HIC
## FRANCE 1.2998 [1.2993; 1.3003] 3.3 1.7 HIC
## GERMANY 1.3422 [1.3418; 1.3426] 4.4 1.7 HIC
## GREECE 0.7983 [0.7974; 0.7991] 0.4 1.7 HIC
## HUNGARY 0.2731 [0.2725; 0.2736] 0.1 1.7 HIC
## INDIA 0.0099 [0.0099; 0.0099] 0.5 1.7 LMIC
## IRELAND 0.9042 [0.9028; 0.9057] 0.2 1.7 HIC
## ITALY 0.5370 [0.5367; 0.5373] 1.3 1.7 HIC
## JAPAN 0.1478 [0.1477; 0.1479] 0.8 1.7 HIC
## JORDAN 0.0886 [0.0882; 0.0889] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0266 [0.0263; 0.0269] 0.0 1.7 HIC
## LATVIA 0.3719 [0.3705; 0.3732] 0.0 1.7 HIC
## LEBANON 0.3655 [0.3646; 0.3664] 0.1 1.7 UMIC
## LITHUANIA 0.2225 [0.2216; 0.2234] 0.0 1.7 HIC
## LUXEMBOURG 0.7788 [0.7747; 0.7829] 0.0 1.7 HIC
## MEXICO 0.0599 [0.0598; 0.0600] 0.3 1.7 UMIC
## MOROCCO 0.0051 [0.0050; 0.0051] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.1650 [1.1633; 1.1667] 0.2 1.7 HIC
## NORWAY 1.4251 [1.4233; 1.4268] 0.3 1.7 HIC
## PAKISTAN 0.0466 [0.0465; 0.0466] 0.3 1.7 LMIC
## PERU 0.0173 [0.0173; 0.0174] 0.0 1.7 UMIC
## PHILIPPINES 0.0266 [0.0265; 0.0266] 0.1 1.7 LMIC
## POLAND 0.1674 [0.1672; 0.1676] 0.3 1.7 HIC
## PORTUGAL 1.1282 [1.1271; 1.1292] 0.5 1.7 HIC
## PUERTO RICO 4.7038 [4.7000; 4.7075] 0.7 1.7 HIC
## ROMANIA 0.1543 [0.1540; 0.1546] 0.1 1.7 UMIC
## RUSSIA 0.0166 [0.0166; 0.0166] 0.1 1.7 UMIC
## SAUDI ARABIA 0.1008 [0.1006; 0.1010] 0.1 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.7215 [0.7203; 0.7227] 0.2 1.7 HIC
## SLOVENIA 0.4132 [0.4117; 0.4146] 0.0 1.7 HIC
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.7 UMIC
## SOUTH KOREA 0.6597 [0.6593; 0.6601] 1.3 1.7 HIC
## SPAIN 1.8188 [1.8181; 1.8194] 3.4 1.7 HIC
## SWEDEN 1.2153 [1.2141; 1.2165] 0.5 1.7 HIC
## SWITZERLAND 0.5953 [0.5944; 0.5962] 0.2 1.7 HIC
## TAIWAN 0.1779 [0.1776; 0.1781] 0.2 1.7 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0831 [0.0828; 0.0834] 0.0 1.7 LMIC
## TÜRKIYE 1.4377 [1.4372; 1.4381] 4.1 1.7 UMIC
## UNITED ARAB EMIRATES 0.1115 [0.1111; 0.1119] 0.0 1.7 HIC
## UNITED KINGDOM 2.0046 [2.0040; 2.0052] 5.1 1.7 HIC
## UNITED STATES 5.1806 [5.1802; 5.1811] 63.7 1.7 HIC
## URUGUAY 0.2686 [0.2677; 0.2695] 0.0 1.7 HIC
## VENEZUELA 0.4848 [0.4844; 0.4852] 0.5 1.7 UMIC
##
## Number of studies combined: k = 59
##
## rate 95%-CI z p-value
## Common effect model 2.8470 [2.8469; 2.8472] 31556.37 0
## Random effects model 0.2235 [0.1569; 0.3183] -8.30 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.9211 [1.6536; 5.4182]; tau = 1.3860 [1.2859; 2.3277]
## I^2 = 100.0%; H = 4193.46
##
## Test of heterogeneity:
## Q d.f. p-value
## 1019937617.68 58 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 3.3765 [3.3762; 3.3767] 468190078.46 100.0%
## income = UMIC 16 0.5661 [0.5660; 0.5663] 130524285.36 100.0%
## income = LMIC 6 0.0276 [0.0276; 0.0276] 9024236.18 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 412199017.69 2 0
## Within groups 607738599.99 56 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.5381 [0.3850; 0.7520] 1.0788 1.0386
## income = UMIC 16 0.0643 [0.0236; 0.1748] 4.1692 2.0419
## income = LMIC 6 0.0275 [0.0117; 0.0644] 1.1341 1.0649
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 51.00 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1292 [0.1290; 0.1294] 0.2 1.7 UMIC
## AUSTRALIA 0.8366 [0.8360; 0.8373] 0.6 1.7 HIC
## AUSTRIA 1.7240 [1.7225; 1.7255] 0.5 1.7 HIC
## BELARUS 0.0122 [0.0121; 0.0124] 0.0 1.7 UMIC
## BELGIUM 0.4468 [0.4461; 0.4475] 0.2 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0614 [0.0614; 0.0615] 0.4 1.7 UMIC
## BULGARIA 0.1616 [0.1611; 0.1621] 0.0 1.7 UMIC
## CANADA 2.7823 [2.7814; 2.7832] 3.2 1.7 HIC
## CHILE 0.0269 [0.0268; 0.0271] 0.0 1.7 HIC
## CHINA 0.0031 [0.0031; 0.0031] 0.1 1.7 UMIC
## COLOMBIA 0.0235 [0.0234; 0.0236] 0.0 1.7 UMIC
## CROATIA 0.2668 [0.2660; 0.2676] 0.0 1.7 HIC
## CZECH REPUBLIC 1.0463 [1.0452; 1.0473] 0.4 1.7 HIC
## ECUADOR 0.0720 [0.0718; 0.0723] 0.0 1.7 UMIC
## EGYPT 0.1069 [0.1067; 0.1070] 0.3 1.7 LMIC
## ESTONIA 0.2425 [0.2411; 0.2439] 0.0 1.7 HIC
## FINLAND 0.9824 [0.9810; 0.9837] 0.2 1.7 HIC
## FRANCE 1.2534 [1.2529; 1.2538] 2.7 1.7 HIC
## GERMANY 1.4106 [1.4101; 1.4110] 3.9 1.7 HIC
## GREECE 0.8032 [0.8023; 0.8041] 0.3 1.7 HIC
## HUNGARY 0.3559 [0.3553; 0.3565] 0.1 1.7 HIC
## INDIA 0.0114 [0.0114; 0.0114] 0.5 1.7 LMIC
## IRELAND 0.8461 [0.8447; 0.8475] 0.1 1.7 HIC
## ITALY 0.5452 [0.5449; 0.5455] 1.1 1.7 HIC
## JAPAN 0.1810 [0.1809; 0.1811] 0.8 1.7 HIC
## JORDAN 0.1086 [0.1082; 0.1090] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0240 [0.0237; 0.0243] 0.0 1.7 HIC
## LATVIA 0.4715 [0.4699; 0.4730] 0.0 1.7 HIC
## LEBANON 0.3637 [0.3629; 0.3646] 0.1 1.7 UMIC
## LITHUANIA 0.2311 [0.2302; 0.2320] 0.0 1.7 HIC
## LUXEMBOURG 0.7427 [0.7388; 0.7466] 0.0 1.7 HIC
## MEXICO 0.0597 [0.0596; 0.0597] 0.2 1.7 UMIC
## MOROCCO 0.0060 [0.0060; 0.0061] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.3931 [1.3913; 1.3950] 0.2 1.7 HIC
## NORWAY 1.8065 [1.8046; 1.8085] 0.3 1.7 HIC
## PAKISTAN 0.0446 [0.0445; 0.0446] 0.3 1.7 LMIC
## PERU 0.0172 [0.0171; 0.0173] 0.0 1.7 UMIC
## PHILIPPINES 0.0290 [0.0289; 0.0291] 0.1 1.7 LMIC
## POLAND 0.1920 [0.1917; 0.1922] 0.2 1.7 HIC
## PORTUGAL 1.1608 [1.1597; 1.1618] 0.4 1.7 HIC
## PUERTO RICO 5.5328 [5.5288; 5.5368] 0.7 1.7 HIC
## ROMANIA 0.2463 [0.2459; 0.2466] 0.2 1.7 UMIC
## RUSSIA 0.0159 [0.0159; 0.0160] 0.1 1.7 UMIC
## SAUDI ARABIA 0.1232 [0.1230; 0.1234] 0.1 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.8791 [0.8778; 0.8805] 0.2 1.7 HIC
## SLOVENIA 0.4079 [0.4064; 0.4093] 0.0 1.7 HIC
## SOUTH AFRICA 0.0456 [0.0455; 0.0457] 0.1 1.7 UMIC
## SOUTH KOREA 0.6761 [0.6757; 0.6765] 1.1 1.7 HIC
## SPAIN 1.8213 [1.8206; 1.8219] 2.9 1.7 HIC
## SWEDEN 1.2623 [1.2611; 1.2635] 0.4 1.7 HIC
## SWITZERLAND 0.5748 [0.5739; 0.5757] 0.2 1.7 HIC
## TAIWAN 0.1970 [0.1967; 0.1973] 0.2 1.7 HIC
## THAILAND 0.1721 [0.1720; 0.1723] 0.4 1.7 UMIC
## TUNISIA 0.0815 [0.0812; 0.0818] 0.0 1.7 LMIC
## TÜRKIYE 1.7746 [1.7740; 1.7751] 4.3 1.7 UMIC
## UNITED ARAB EMIRATES 0.1305 [0.1301; 0.1309] 0.0 1.7 HIC
## UNITED KINGDOM 2.3586 [2.3580; 2.3592] 5.1 1.7 HIC
## UNITED STATES 6.3255 [6.3250; 6.3260] 66.0 1.7 HIC
## URUGUAY 0.2401 [0.2392; 0.2409] 0.0 1.7 HIC
## VENEZUELA 0.4784 [0.4780; 0.4788] 0.5 1.7 UMIC
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.4476 [3.4474; 3.4478] 40696.65 0
## Random effects model 0.2445 [0.1685; 0.3549] -7.41 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.1673 [1.7147; 5.6231]; tau = 1.4722 [1.3094; 2.3713]
## I^2 = 100.0%; H = 4694.40
##
## Test of heterogeneity:
## Q d.f. p-value
## 1300205778.35 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 4.1294 [4.1291; 4.1296] 602945599.95 100.0%
## income = UMIC 17 0.6556 [0.6555; 0.6558] 173545390.03 100.0%
## income = LMIC 6 0.0311 [0.0311; 0.0311] 10962499.21 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 512752289.16 2 0
## Within groups 787453489.19 57 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.5882 [0.4093; 0.8453] 1.2663 1.1253
## income = UMIC 17 0.0756 [0.0284; 0.2015] 4.2469 2.0608
## income = LMIC 6 0.0303 [0.0125; 0.0733] 1.2171 1.1032
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 46.54 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1364 [0.1363; 0.1366] 0.2 1.6 UMIC
## AUSTRALIA 0.8668 [0.8662; 0.8675] 0.6 1.6 HIC
## AUSTRIA 1.8391 [1.8376; 1.8406] 0.5 1.6 HIC
## BELARUS 0.0139 [0.0138; 0.0140] 0.0 1.6 UMIC
## BELGIUM 0.4884 [0.4878; 0.4891] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0443 [0.0439; 0.0447] 0.0 1.6 UMIC
## BRAZIL 0.0770 [0.0769; 0.0770] 0.5 1.6 UMIC
## BULGARIA 0.2211 [0.2206; 0.2217] 0.1 1.6 UMIC
## CANADA 2.9905 [2.9896; 2.9915] 3.3 1.6 HIC
## CHILE 0.0234 [0.0233; 0.0235] 0.0 1.6 HIC
## CHINA 0.0047 [0.0047; 0.0047] 0.2 1.6 UMIC
## COLOMBIA 0.0219 [0.0218; 0.0220] 0.0 1.6 UMIC
## CROATIA 0.2282 [0.2275; 0.2290] 0.0 1.6 HIC
## CZECH REPUBLIC 1.2108 [1.2097; 1.2119] 0.4 1.6 HIC
## ECUADOR 0.0672 [0.0670; 0.0675] 0.0 1.6 UMIC
## EGYPT 0.1148 [0.1147; 0.1149] 0.3 1.6 LMIC
## ESTONIA 0.2902 [0.2887; 0.2917] 0.0 1.6 HIC
## FINLAND 1.0435 [1.0420; 1.0449] 0.2 1.6 HIC
## FRANCE 1.2042 [1.2038; 1.2047] 2.4 1.6 HIC
## GERMANY 1.4962 [1.4957; 1.4966] 3.8 1.6 HIC
## GREECE 0.8916 [0.8906; 0.8925] 0.3 1.6 HIC
## HUNGARY 0.4230 [0.4223; 0.4236] 0.1 1.6 HIC
## INDIA 0.0135 [0.0135; 0.0136] 0.5 1.6 LMIC
## IRELAND 0.8952 [0.8938; 0.8967] 0.1 1.6 HIC
## ITALY 0.5448 [0.5445; 0.5451] 1.0 1.6 HIC
## JAPAN 0.1505 [0.1504; 0.1506] 0.6 1.6 HIC
## JORDAN 0.1083 [0.1079; 0.1087] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0189 [0.0187; 0.0190] 0.0 1.6 UMIC
## KUWAIT 0.0285 [0.0282; 0.0288] 0.0 1.6 HIC
## LATVIA 0.6091 [0.6073; 0.6108] 0.0 1.6 HIC
## LEBANON 0.3338 [0.3330; 0.3346] 0.1 1.6 UMIC
## LITHUANIA 0.2378 [0.2369; 0.2387] 0.0 1.6 HIC
## LUXEMBOURG 0.7418 [0.7379; 0.7456] 0.0 1.6 HIC
## MEXICO 0.0580 [0.0579; 0.0581] 0.2 1.6 UMIC
## MOROCCO 0.0104 [0.0103; 0.0104] 0.0 1.6 LMIC
## NETHERLANDS 0.6528 [0.6522; 0.6534] 0.3 1.6 HIC
## NEW ZEALAND 1.6064 [1.6045; 1.6084] 0.2 1.6 HIC
## NORWAY 2.0397 [2.0376; 2.0418] 0.3 1.6 HIC
## PAKISTAN 0.0465 [0.0465; 0.0466] 0.3 1.6 LMIC
## PERU 0.0192 [0.0192; 0.0193] 0.0 1.6 UMIC
## PHILIPPINES 0.0279 [0.0279; 0.0280] 0.1 1.6 LMIC
## POLAND 0.2211 [0.2208; 0.2213] 0.3 1.6 HIC
## PORTUGAL 1.1147 [1.1136; 1.1157] 0.4 1.6 HIC
## PUERTO RICO 7.1769 [7.1723; 7.1815] 0.8 1.6 HIC
## ROMANIA 0.3992 [0.3987; 0.3996] 0.3 1.6 UMIC
## RUSSIA 0.0184 [0.0183; 0.0184] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1683 [0.1681; 0.1686] 0.2 1.6 HIC
## SERBIA 0.0660 [0.0657; 0.0663] 0.0 1.6 UMIC
## SLOVAKIA 0.9711 [0.9697; 0.9724] 0.2 1.6 HIC
## SLOVENIA 0.3702 [0.3688; 0.3715] 0.0 1.6 HIC
## SOUTH AFRICA 0.0503 [0.0502; 0.0504] 0.1 1.6 UMIC
## SOUTH KOREA 0.6730 [0.6727; 0.6734] 1.1 1.6 HIC
## SPAIN 1.8636 [1.8630; 1.8643] 2.8 1.6 HIC
## SWEDEN 1.4097 [1.4084; 1.4109] 0.4 1.6 HIC
## SWITZERLAND 0.5369 [0.5360; 0.5377] 0.1 1.6 HIC
## TAIWAN 0.2174 [0.2171; 0.2177] 0.2 1.6 HIC
## THAILAND 0.2549 [0.2547; 0.2551] 0.5 1.6 UMIC
## TUNISIA 0.0891 [0.0888; 0.0894] 0.0 1.6 LMIC
## TÜRKIYE 2.1980 [2.1975; 2.1986] 5.1 1.6 UMIC
## UNITED ARAB EMIRATES 0.1306 [0.1302; 0.1310] 0.0 1.6 HIC
## UNITED KINGDOM 2.7146 [2.7139; 2.7152] 5.5 1.6 HIC
## UNITED STATES 6.5599 [6.5594; 6.5604] 64.5 1.6 HIC
## URUGUAY 0.3039 [0.3029; 0.3049] 0.0 1.6 HIC
## VENEZUELA 0.5095 [0.5091; 0.5100] 0.5 1.6 UMIC
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.5448 [3.5446; 3.5450] 43059.23 0
## Random effects model 0.2490 [0.1740; 0.3565] -7.60 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.1427 [1.7383; 5.5270]; tau = 1.4638 [1.3185; 2.3510]
## I^2 = 100.0%; H = 4754.58
##
## Test of heterogeneity:
## Q d.f. p-value
## 1424179537.86 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 4.2949 [4.2946; 4.2951] 635255138.42 100.0%
## income = UMIC 20 0.8007 [0.8005; 0.8008] 225245070.39 100.0%
## income = LMIC 6 0.0331 [0.0330; 0.0331] 11322037.57 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 552357291.48 2 0
## Within groups 871822246.38 61 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.6297 [0.4422; 0.8968] 1.2366 1.1120
## income = UMIC 20 0.0769 [0.0307; 0.1929] 4.3982 2.0972
## income = LMIC 6 0.0351 [0.0150; 0.0823] 1.1340 1.0649
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 49.00 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1383 [0.1382; 0.1385] 0.2 1.6 UMIC
## AUSTRALIA 0.9460 [0.9453; 0.9466] 0.7 1.6 HIC
## AUSTRIA 1.9626 [1.9611; 1.9642] 0.5 1.6 HIC
## BELARUS 0.0148 [0.0147; 0.0150] 0.0 1.6 UMIC
## BELGIUM 0.5723 [0.5715; 0.5730] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0958 [0.0953; 0.0964] 0.0 1.6 UMIC
## BRAZIL 0.0876 [0.0875; 0.0876] 0.5 1.6 UMIC
## BULGARIA 0.2866 [0.2859; 0.2872] 0.1 1.6 UMIC
## CANADA 3.3044 [3.3034; 3.3054] 3.6 1.6 HIC
## CHILE 0.0220 [0.0219; 0.0221] 0.0 1.6 HIC
## CHINA 0.0083 [0.0083; 0.0083] 0.4 1.6 UMIC
## COLOMBIA 0.0193 [0.0192; 0.0193] 0.0 1.6 UMIC
## CROATIA 0.2042 [0.2035; 0.2049] 0.0 1.6 HIC
## CZECH REPUBLIC 1.3418 [1.3407; 1.3430] 0.4 1.6 HIC
## ECUADOR 0.0590 [0.0588; 0.0592] 0.0 1.6 UMIC
## EGYPT 0.1044 [0.1043; 0.1045] 0.3 1.6 LMIC
## ESTONIA 0.3901 [0.3883; 0.3919] 0.0 1.6 HIC
## FINLAND 1.1761 [1.1746; 1.1776] 0.2 1.6 HIC
## FRANCE 1.2173 [1.2168; 1.2177] 2.4 1.6 HIC
## GERMANY 1.5603 [1.5599; 1.5608] 3.9 1.6 HIC
## GREECE 0.8239 [0.8230; 0.8248] 0.3 1.6 HIC
## HUNGARY 0.4641 [0.4634; 0.4648] 0.1 1.6 HIC
## INDIA 0.0145 [0.0145; 0.0145] 0.6 1.6 LMIC
## IRELAND 0.9214 [0.9199; 0.9228] 0.1 1.6 HIC
## ITALY 0.5310 [0.5307; 0.5313] 1.0 1.6 HIC
## JAPAN 0.1244 [0.1243; 0.1245] 0.5 1.6 HIC
## JORDAN 0.0998 [0.0994; 0.1002] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0218 [0.0217; 0.0220] 0.0 1.6 UMIC
## KUWAIT 0.0292 [0.0289; 0.0295] 0.0 1.6 HIC
## LATVIA 0.8136 [0.8115; 0.8156] 0.1 1.6 HIC
## LEBANON 0.2992 [0.2985; 0.3000] 0.1 1.6 UMIC
## LITHUANIA 0.2617 [0.2607; 0.2626] 0.0 1.6 HIC
## LUXEMBOURG 0.7475 [0.7437; 0.7514] 0.0 1.6 HIC
## MEXICO 0.0640 [0.0639; 0.0641] 0.2 1.6 UMIC
## MOROCCO 0.0172 [0.0171; 0.0173] 0.0 1.6 LMIC
## NETHERLANDS 0.6605 [0.6599; 0.6612] 0.3 1.6 HIC
## NEW ZEALAND 1.8861 [1.8840; 1.8882] 0.3 1.6 HIC
## NORWAY 2.1749 [2.1727; 2.1770] 0.3 1.6 HIC
## PAKISTAN 0.0469 [0.0469; 0.0470] 0.3 1.6 LMIC
## PERU 0.0190 [0.0189; 0.0191] 0.0 1.6 UMIC
## PHILIPPINES 0.0268 [0.0267; 0.0268] 0.1 1.6 LMIC
## POLAND 0.2055 [0.2053; 0.2058] 0.2 1.6 HIC
## PORTUGAL 1.1942 [1.1931; 1.1953] 0.4 1.6 HIC
## PUERTO RICO 8.2103 [8.2053; 8.2152] 0.9 1.6 HIC
## ROMANIA 0.5151 [0.5146; 0.5157] 0.3 1.6 UMIC
## RUSSIA 0.0226 [0.0225; 0.0226] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1279 [0.1277; 0.1281] 0.1 1.6 HIC
## SERBIA 0.0827 [0.0823; 0.0830] 0.0 1.6 UMIC
## SLOVAKIA 1.0650 [1.0635; 1.0664] 0.2 1.6 HIC
## SLOVENIA 0.3544 [0.3531; 0.3558] 0.0 1.6 HIC
## SOUTH AFRICA 0.0532 [0.0531; 0.0533] 0.1 1.6 UMIC
## SOUTH KOREA 0.7229 [0.7225; 0.7232] 1.1 1.6 HIC
## SPAIN 1.8152 [1.8146; 1.8159] 2.7 1.6 HIC
## SWEDEN 1.5277 [1.5265; 1.5290] 0.5 1.6 HIC
## SWITZERLAND 0.5176 [0.5168; 0.5184] 0.1 1.6 HIC
## TAIWAN 0.2142 [0.2139; 0.2145] 0.2 1.6 HIC
## THAILAND 0.2950 [0.2948; 0.2952] 0.6 1.6 UMIC
## TUNISIA 0.1064 [0.1061; 0.1067] 0.0 1.6 LMIC
## TÜRKIYE 2.3781 [2.3775; 2.3787] 5.5 1.6 UMIC
## UNITED ARAB EMIRATES 0.0810 [0.0807; 0.0813] 0.0 1.6 HIC
## UNITED KINGDOM 3.2081 [3.2074; 3.2089] 6.4 1.6 HIC
## UNITED STATES 6.3591 [6.3586; 6.3595] 62.0 1.6 HIC
## URUGUAY 0.4181 [0.4169; 0.4192] 0.0 1.6 HIC
## VENEZUELA 0.5864 [0.5860; 0.5869] 0.5 1.6 UMIC
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.4442 [3.4440; 3.4444] 42403.36 0
## Random effects model 0.2674 [0.1883; 0.3797] -7.37 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.0489 [1.7405; 5.4639]; tau = 1.4314 [1.3193; 2.3375]
## I^2 = 100.0%; H = 4801.89
##
## Test of heterogeneity:
## Q d.f. p-value
## 1452664566.87 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 4.2291 [4.2288; 4.2293] 594103079.71 100.0%
## income = UMIC 20 0.8188 [0.8186; 0.8190] 267766913.87 100.0%
## income = LMIC 6 0.0321 [0.0320; 0.0321] 9989612.85 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 580804960.43 2 0
## Within groups 871859606.44 61 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.6506 [0.4666; 0.9072] 1.0932 1.0456
## income = UMIC 20 0.0880 [0.0350; 0.2214] 4.4326 2.1054
## income = LMIC 6 0.0389 [0.0177; 0.0858] 0.9748 0.9873
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 51.57 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0000 0.0 0.0 UMIC
## ARGENTINA 0.1348 [0.1346; 0.1350] 0.2 1.6 UMIC
## AUSTRALIA 0.7847 [0.7841; 0.7853] 0.5 1.6 HIC
## AUSTRIA 2.0660 [2.0644; 2.0676] 0.5 1.6 HIC
## BELARUS 0.0221 [0.0219; 0.0222] 0.0 1.6 UMIC
## BELGIUM 0.8101 [0.8093; 0.8110] 0.2 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.1360 [0.1353; 0.1366] 0.0 1.6 UMIC
## BRAZIL 0.0978 [0.0977; 0.0978] 0.5 1.6 UMIC
## BULGARIA 0.4003 [0.3995; 0.4010] 0.1 1.6 UMIC
## CANADA 3.6682 [3.6672; 3.6693] 3.5 1.6 HIC
## CHILE 0.0206 [0.0205; 0.0207] 0.0 1.6 HIC
## CHINA 0.0119 [0.0119; 0.0119] 0.5 1.6 UMIC
## COLOMBIA 0.0168 [0.0167; 0.0169] 0.0 1.6 UMIC
## CROATIA 0.1891 [0.1884; 0.1897] 0.0 1.6 HIC
## CZECH REPUBLIC 1.5003 [1.4991; 1.5015] 0.4 1.6 HIC
## ECUADOR 0.0490 [0.0488; 0.0492] 0.0 1.6 UMIC
## EGYPT 0.1103 [0.1102; 0.1104] 0.3 1.6 LMIC
## ESTONIA 0.5081 [0.5061; 0.5101] 0.0 1.6 HIC
## FINLAND 1.4016 [1.4000; 1.4032] 0.2 1.6 HIC
## FRANCE 1.2245 [1.2240; 1.2249] 2.1 1.6 HIC
## GERMANY 1.6432 [1.6427; 1.6436] 3.6 1.6 HIC
## GREECE 0.9298 [0.9289; 0.9308] 0.3 1.6 HIC
## HUNGARY 0.5382 [0.5374; 0.5389] 0.1 1.6 HIC
## INDIA 0.0157 [0.0157; 0.0157] 0.5 1.6 LMIC
## IRELAND 0.9678 [0.9663; 0.9693] 0.1 1.6 HIC
## ITALY 0.5287 [0.5284; 0.5290] 0.9 1.6 HIC
## JAPAN 0.1114 [0.1113; 0.1115] 0.4 1.6 HIC
## JORDAN 0.1073 [0.1069; 0.1076] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0203 [0.0202; 0.0204] 0.0 1.6 UMIC
## KUWAIT 0.0408 [0.0404; 0.0411] 0.0 1.6 HIC
## LATVIA 1.0487 [1.0464; 1.0510] 0.1 1.6 HIC
## LEBANON 0.3187 [0.3180; 0.3195] 0.1 1.6 UMIC
## LITHUANIA 0.3129 [0.3118; 0.3139] 0.0 1.6 HIC
## LUXEMBOURG 0.7067 [0.7030; 0.7104] 0.0 1.6 HIC
## MEXICO 0.0705 [0.0704; 0.0705] 0.2 1.6 UMIC
## MOROCCO 0.0270 [0.0269; 0.0271] 0.0 1.6 LMIC
## NETHERLANDS 0.6591 [0.6585; 0.6598] 0.3 1.6 HIC
## NEW ZEALAND 2.1377 [2.1355; 2.1399] 0.3 1.6 HIC
## NORWAY 2.2712 [2.2690; 2.2734] 0.3 1.6 HIC
## PAKISTAN 0.0450 [0.0449; 0.0450] 0.2 1.6 LMIC
## PERU 0.0181 [0.0180; 0.0182] 0.0 1.6 UMIC
## PHILIPPINES 0.0330 [0.0330; 0.0331] 0.1 1.6 LMIC
## POLAND 0.2660 [0.2658; 0.2663] 0.3 1.6 HIC
## PORTUGAL 1.2241 [1.2230; 1.2252] 0.3 1.6 HIC
## PUERTO RICO 8.3869 [8.3819; 8.3919] 0.8 1.6 HIC
## ROMANIA 0.6903 [0.6897; 0.6909] 0.4 1.6 UMIC
## RUSSIA 0.0296 [0.0295; 0.0296] 0.1 1.6 UMIC
## SAUDI ARABIA 0.1192 [0.1190; 0.1194] 0.1 1.6 HIC
## SERBIA 0.0894 [0.0891; 0.0897] 0.0 1.6 UMIC
## SLOVAKIA 1.1862 [1.1847; 1.1877] 0.2 1.6 HIC
## SLOVENIA 0.3501 [0.3488; 0.3515] 0.0 1.6 HIC
## SOUTH AFRICA 0.0572 [0.0571; 0.0573] 0.1 1.6 UMIC
## SOUTH KOREA 0.7358 [0.7354; 0.7362] 1.0 1.6 HIC
## SPAIN 1.8345 [1.8339; 1.8352] 2.3 1.6 HIC
## SWEDEN 1.8971 [1.8957; 1.8986] 0.5 1.6 HIC
## SWITZERLAND 0.5102 [0.5094; 0.5110] 0.1 1.6 HIC
## TAIWAN 0.2000 [0.1997; 0.2003] 0.1 1.6 HIC
## THAILAND 0.4366 [0.4363; 0.4368] 0.8 1.6 UMIC
## TUNISIA 0.1107 [0.1104; 0.1111] 0.0 1.6 LMIC
## TÜRKIYE 2.5445 [2.5439; 2.5451] 5.3 1.6 UMIC
## UNITED ARAB EMIRATES 0.0828 [0.0825; 0.0831] 0.0 1.6 HIC
## UNITED KINGDOM 3.8409 [3.8401; 3.8417] 6.8 1.6 HIC
## UNITED STATES 7.3525 [7.3520; 7.3530] 63.3 1.6 HIC
## URUGUAY 0.4117 [0.4106; 0.4128] 0.0 1.6 HIC
## VENEZUELA 0.6841 [0.6836; 0.6846] 0.6 1.6 UMIC
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 3.9829 [3.9827; 3.9831] 50618.55 0
## Random effects model 0.2942 [0.2051; 0.4220] -6.65 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.1685 [1.7671; 5.5888]; tau = 1.4726 [1.3293; 2.3641]
## I^2 = 100.0%; H = 5207.29
##
## Test of heterogeneity:
## Q d.f. p-value
## 1708299477.71 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 4.9391 [4.9388; 4.9393] 690808095.36 100.0%
## income = UMIC 20 0.8484 [0.8483; 0.8486] 303086348.56 100.0%
## income = LMIC 6 0.0336 [0.0336; 0.0336] 10213923.61 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 704191110.17 2 0
## Within groups 1004108367.53 61 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.6983 [0.4959; 0.9833] 1.1591 1.0766
## income = UMIC 20 0.1004 [0.0414; 0.2439] 4.0996 2.0247
## income = LMIC 6 0.0444 [0.0206; 0.0956] 0.9183 0.9583
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 50.89 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0031 [ 0.0031; 0.0032] 0.0 1.5 UMIC
## ARGENTINA 0.1286 [ 0.1284; 0.1288] 0.1 1.5 UMIC
## AUSTRALIA 0.6459 [ 0.6453; 0.6464] 0.4 1.5 HIC
## AUSTRIA 2.1770 [ 2.1754; 2.1786] 0.5 1.5 HIC
## BELARUS 0.0305 [ 0.0303; 0.0307] 0.0 1.5 UMIC
## BELGIUM 1.0163 [ 1.0154; 1.0173] 0.3 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1421 [ 0.1414; 0.1427] 0.0 1.5 UMIC
## BRAZIL 0.1063 [ 0.1062; 0.1064] 0.5 1.5 UMIC
## BULGARIA 0.4875 [ 0.4867; 0.4884] 0.1 1.5 UMIC
## CANADA 3.8718 [ 3.8707; 3.8729] 3.5 1.5 HIC
## CHILE 0.0202 [ 0.0201; 0.0203] 0.0 1.5 HIC
## CHINA 0.0158 [ 0.0158; 0.0158] 0.6 1.5 UMIC
## COLOMBIA 0.0150 [ 0.0150; 0.0151] 0.0 1.5 UMIC
## CROATIA 0.1837 [ 0.1830; 0.1844] 0.0 1.5 HIC
## CZECH REPUBLIC 1.7140 [ 1.7127; 1.7153] 0.5 1.5 HIC
## ECUADOR 0.0434 [ 0.0432; 0.0436] 0.0 1.5 UMIC
## EGYPT 0.1243 [ 0.1242; 0.1244] 0.3 1.5 LMIC
## ESTONIA 0.6371 [ 0.6349; 0.6394] 0.0 1.5 HIC
## FINLAND 1.6351 [ 1.6333; 1.6368] 0.2 1.5 HIC
## FRANCE 1.2639 [ 1.2634; 1.2644] 2.0 1.5 HIC
## GERMANY 1.6853 [ 1.6848; 1.6858] 3.4 1.5 HIC
## GREECE 0.9639 [ 0.9629; 0.9649] 0.3 1.5 HIC
## HUNGARY 0.6103 [ 0.6095; 0.6111] 0.1 1.5 HIC
## INDIA 0.0183 [ 0.0183; 0.0183] 0.6 1.5 LMIC
## IRELAND 1.0037 [ 1.0022; 1.0052] 0.1 1.5 HIC
## ITALY 0.5519 [ 0.5516; 0.5522] 0.8 1.5 HIC
## JAPAN 0.1038 [ 0.1037; 0.1039] 0.3 1.5 HIC
## JORDAN 0.0884 [ 0.0880; 0.0887] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0272 [ 0.0270; 0.0273] 0.0 1.5 UMIC
## KUWAIT 0.1028 [ 0.1022; 0.1033] 0.0 1.5 HIC
## LATVIA 1.2957 [ 1.2931; 1.2983] 0.1 1.5 HIC
## LEBANON 0.3372 [ 0.3365; 0.3380] 0.1 1.5 UMIC
## LITHUANIA 0.3355 [ 0.3344; 0.3366] 0.0 1.5 HIC
## LUXEMBOURG 0.6660 [ 0.6625; 0.6696] 0.0 1.5 HIC
## MEXICO 0.0697 [ 0.0696; 0.0698] 0.2 1.5 UMIC
## MOROCCO 0.0311 [ 0.0310; 0.0312] 0.0 1.5 LMIC
## NETHERLANDS 0.6984 [ 0.6977; 0.6991] 0.3 1.5 HIC
## NEW ZEALAND 2.4379 [ 2.4355; 2.4403] 0.3 1.5 HIC
## NORWAY 2.2095 [ 2.2074; 2.2116] 0.3 1.5 HIC
## PAKISTAN 0.0423 [ 0.0422; 0.0423] 0.2 1.5 LMIC
## PERU 0.0158 [ 0.0157; 0.0159] 0.0 1.5 UMIC
## PHILIPPINES 0.0272 [ 0.0271; 0.0272] 0.1 1.5 LMIC
## POLAND 0.3332 [ 0.3329; 0.3335] 0.3 1.5 HIC
## PORTUGAL 1.2655 [ 1.2643; 1.2666] 0.3 1.5 HIC
## PUERTO RICO 10.1429 [10.1373; 10.1484] 0.9 1.5 HIC
## ROMANIA 0.8419 [ 0.8412; 0.8426] 0.4 1.5 UMIC
## RUSSIA 0.0362 [ 0.0361; 0.0362] 0.1 1.5 UMIC
## SAUDI ARABIA 0.1250 [ 0.1248; 0.1252] 0.1 1.5 HIC
## SERBIA 0.0908 [ 0.0905; 0.0912] 0.0 1.5 UMIC
## SLOVAKIA 1.4205 [ 1.4189; 1.4222] 0.2 1.5 HIC
## SLOVENIA 0.3401 [ 0.3388; 0.3414] 0.0 1.5 HIC
## SOUTH AFRICA 0.0581 [ 0.0580; 0.0582] 0.1 1.5 UMIC
## SOUTH KOREA 0.8166 [ 0.8162; 0.8170] 1.0 1.5 HIC
## SPAIN 1.8863 [ 1.8856; 1.8869] 2.2 1.5 HIC
## SWEDEN 2.3524 [ 2.3508; 2.3540] 0.6 1.5 HIC
## SWITZERLAND 0.5117 [ 0.5109; 0.5125] 0.1 1.5 HIC
## TAIWAN 0.1917 [ 0.1914; 0.1920] 0.1 1.5 HIC
## THAILAND 0.4858 [ 0.4856; 0.4861] 0.8 1.5 UMIC
## TUNISIA 0.1231 [ 0.1227; 0.1234] 0.0 1.5 LMIC
## TÜRKIYE 2.5723 [ 2.5717; 2.5729] 5.0 1.5 UMIC
## UNITED ARAB EMIRATES 0.0897 [ 0.0893; 0.0900] 0.0 1.5 HIC
## UNITED KINGDOM 4.4910 [ 4.4902; 4.4919] 7.4 1.5 HIC
## UNITED STATES 7.9211 [ 7.9206; 7.9216] 63.3 1.5 HIC
## URUGUAY 0.4417 [ 0.4405; 0.4429] 0.0 1.5 HIC
## VENEZUELA 0.8366 [ 0.8360; 0.8371] 0.6 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.2944 [4.2941; 4.2946] 55636.38 0
## Random effects model 0.2970 [0.2069; 0.4264] -6.58 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.2140 [1.8122; 5.7187]; tau = 1.4879 [1.3462; 2.3914]
## I^2 = 100.0%; H = 5444.97
##
## Test of heterogeneity:
## Q d.f. p-value
## 1897453214.78 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 5.3741 [5.3738; 5.3744] 745368761.93 100.0%
## income = UMIC 21 0.8334 [0.8333; 0.8336] 327035689.48 100.0%
## income = LMIC 6 0.0360 [0.0360; 0.0360] 11188677.96 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 813860085.40 2 0
## Within groups 1083593129.38 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.7651 [0.5442; 1.0757] 1.1482 1.0715
## income = UMIC 21 0.0911 [0.0391; 0.2123] 3.9145 1.9785
## income = LMIC 6 0.0464 [0.0213; 0.1010] 0.9425 0.9708
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 55.20 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0272 [ 0.0271; 0.0273] 0.0 1.5 UMIC
## ARGENTINA 0.1302 [ 0.1300; 0.1303] 0.1 1.5 UMIC
## AUSTRALIA 0.6512 [ 0.6507; 0.6518] 0.3 1.5 HIC
## AUSTRIA 2.1760 [ 2.1744; 2.1777] 0.4 1.5 HIC
## BELARUS 0.0476 [ 0.0474; 0.0478] 0.0 1.5 UMIC
## BELGIUM 1.0938 [ 1.0927; 1.0948] 0.3 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1982 [ 0.1974; 0.1990] 0.0 1.5 UMIC
## BRAZIL 0.1118 [ 0.1118; 0.1119] 0.5 1.5 UMIC
## BULGARIA 0.5419 [ 0.5410; 0.5428] 0.1 1.5 UMIC
## CANADA 4.1539 [ 4.1528; 4.1550] 3.3 1.5 HIC
## CHILE 0.0193 [ 0.0192; 0.0194] 0.0 1.5 HIC
## CHINA 0.0202 [ 0.0202; 0.0202] 0.6 1.5 UMIC
## COLOMBIA 0.0150 [ 0.0149; 0.0150] 0.0 1.5 UMIC
## CROATIA 0.1533 [ 0.1527; 0.1539] 0.0 1.5 HIC
## CZECH REPUBLIC 1.8848 [ 1.8834; 1.8861] 0.4 1.5 HIC
## ECUADOR 0.0448 [ 0.0447; 0.0450] 0.0 1.5 UMIC
## EGYPT 0.1340 [ 0.1339; 0.1342] 0.3 1.5 LMIC
## ESTONIA 0.7565 [ 0.7540; 0.7589] 0.0 1.5 HIC
## FINLAND 1.8835 [ 1.8816; 1.8854] 0.2 1.5 HIC
## FRANCE 1.2950 [ 1.2946; 1.2955] 1.8 1.5 HIC
## GERMANY 1.6790 [ 1.6785; 1.6794] 3.0 1.5 HIC
## GREECE 1.0152 [ 1.0142; 1.0162] 0.2 1.5 HIC
## HUNGARY 0.6659 [ 0.6651; 0.6668] 0.1 1.5 HIC
## INDIA 0.0197 [ 0.0197; 0.0197] 0.6 1.5 LMIC
## IRELAND 1.0668 [ 1.0652; 1.0684] 0.1 1.5 HIC
## ITALY 0.5614 [ 0.5610; 0.5617] 0.8 1.5 HIC
## JAPAN 0.0968 [ 0.0967; 0.0969] 0.3 1.5 HIC
## JORDAN 0.0728 [ 0.0725; 0.0731] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0261 [ 0.0259; 0.0262] 0.0 1.5 UMIC
## KUWAIT 0.1504 [ 0.1497; 0.1510] 0.0 1.5 HIC
## LATVIA 1.5928 [ 1.5899; 1.5957] 0.1 1.5 HIC
## LEBANON 0.3553 [ 0.3545; 0.3560] 0.1 1.5 UMIC
## LITHUANIA 0.3776 [ 0.3765; 0.3788] 0.0 1.5 HIC
## LUXEMBOURG 0.6946 [ 0.6910; 0.6982] 0.0 1.5 HIC
## MEXICO 0.0747 [ 0.0746; 0.0748] 0.2 1.5 UMIC
## MOROCCO 0.0421 [ 0.0420; 0.0423] 0.0 1.5 LMIC
## NETHERLANDS 0.7221 [ 0.7215; 0.7228] 0.3 1.5 HIC
## NEW ZEALAND 2.8493 [ 2.8467; 2.8518] 0.3 1.5 HIC
## NORWAY 2.6628 [ 2.6605; 2.6651] 0.3 1.5 HIC
## PAKISTAN 0.0398 [ 0.0397; 0.0398] 0.2 1.5 LMIC
## PERU 0.0558 [ 0.0557; 0.0560] 0.0 1.5 UMIC
## PHILIPPINES 0.0275 [ 0.0274; 0.0275] 0.1 1.5 LMIC
## POLAND 0.4023 [ 0.4020; 0.4027] 0.3 1.5 HIC
## PORTUGAL 1.3129 [ 1.3117; 1.3140] 0.3 1.5 HIC
## PUERTO RICO 11.7963 [11.7902; 11.8024] 0.9 1.5 HIC
## ROMANIA 0.9553 [ 0.9545; 0.9560] 0.4 1.5 UMIC
## RUSSIA 0.0453 [ 0.0453; 0.0454] 0.1 1.5 UMIC
## SAUDI ARABIA 0.2044 [ 0.2042; 0.2047] 0.1 1.5 HIC
## SERBIA 0.1135 [ 0.1131; 0.1138] 0.0 1.5 UMIC
## SLOVAKIA 1.5663 [ 1.5645; 1.5680] 0.2 1.5 HIC
## SLOVENIA 0.3335 [ 0.3322; 0.3348] 0.0 1.5 HIC
## SOUTH AFRICA 0.0551 [ 0.0550; 0.0552] 0.1 1.5 UMIC
## SOUTH KOREA 0.8380 [ 0.8375; 0.8384] 0.9 1.5 HIC
## SPAIN 1.9664 [ 1.9657; 1.9670] 2.0 1.5 HIC
## SWEDEN 2.7401 [ 2.7384; 2.7418] 0.6 1.5 HIC
## SWITZERLAND 0.5081 [ 0.5073; 0.5089] 0.1 1.5 HIC
## TAIWAN 0.2013 [ 0.2010; 0.2016] 0.1 1.5 HIC
## THAILAND 0.5087 [ 0.5084; 0.5090] 0.8 1.5 UMIC
## TUNISIA 0.0958 [ 0.0955; 0.0961] 0.0 1.5 LMIC
## TÜRKIYE 2.6150 [ 2.6144; 2.6156] 4.5 1.5 UMIC
## UNITED ARAB EMIRATES 0.1007 [ 0.1004; 0.1011] 0.0 1.5 HIC
## UNITED KINGDOM 5.1176 [ 5.1167; 5.1185] 7.5 1.5 HIC
## UNITED STATES 9.2026 [ 9.2020; 9.2031] 65.3 1.5 HIC
## URUGUAY 0.3638 [ 0.3627; 0.3649] 0.0 1.5 HIC
## VENEZUELA 0.3521 [ 0.3517; 0.3524] 0.2 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.0243 [5.0241; 5.0246] 65592.18 0
## Random effects model 0.3330 [0.2283; 0.4855] -5.71 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.4074 [1.8765; 5.9757]; tau = 1.5516 [1.3699; 2.4445]
## I^2 = 100.0%; H = 5908.92
##
## Test of heterogeneity:
## Q d.f. p-value
## 2234581732.27 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 6.2883 [6.2879; 6.2886] 880698476.98 100.0%
## income = UMIC 21 0.7779 [0.7777; 0.7780] 359097734.78 100.0%
## income = LMIC 6 0.0374 [0.0374; 0.0375] 11722116.24 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 983063404.27 2 0
## Within groups 1251518328.00 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.8287 [0.5816; 1.1808] 1.2402 1.1137
## income = UMIC 21 0.1115 [0.0463; 0.2684] 4.2216 2.0546
## income = LMIC 6 0.0476 [0.0219; 0.1035] 0.9414 0.9702
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 53.12 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0677 [ 0.0676; 0.0678] 0.1 1.5 UMIC
## ARGENTINA 0.1252 [ 0.1251; 0.1254] 0.1 1.5 UMIC
## AUSTRALIA 0.6366 [ 0.6361; 0.6371] 0.3 1.5 HIC
## AUSTRIA 2.1089 [ 2.1074; 2.1105] 0.4 1.5 HIC
## BELARUS 0.0535 [ 0.0532; 0.0537] 0.0 1.5 UMIC
## BELGIUM 0.9712 [ 0.9703; 0.9722] 0.2 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.2626 [ 0.2617; 0.2635] 0.0 1.5 UMIC
## BRAZIL 0.1122 [ 0.1121; 0.1123] 0.5 1.5 UMIC
## BULGARIA 0.5557 [ 0.5548; 0.5566] 0.1 1.5 UMIC
## CANADA 4.5476 [ 4.5464; 4.5487] 3.3 1.5 HIC
## CHILE 0.0195 [ 0.0194; 0.0196] 0.0 1.5 HIC
## CHINA 0.0238 [ 0.0238; 0.0238] 0.7 1.5 UMIC
## COLOMBIA 0.0147 [ 0.0147; 0.0148] 0.0 1.5 UMIC
## CROATIA 0.1439 [ 0.1433; 0.1445] 0.0 1.5 HIC
## CZECH REPUBLIC 1.9731 [ 1.9717; 1.9745] 0.4 1.5 HIC
## ECUADOR 0.0484 [ 0.0483; 0.0486] 0.0 1.5 UMIC
## EGYPT 0.1568 [ 0.1567; 0.1570] 0.3 1.5 LMIC
## ESTONIA 0.8935 [ 0.8908; 0.8961] 0.0 1.5 HIC
## FINLAND 2.2200 [ 2.2180; 2.2221] 0.2 1.5 HIC
## FRANCE 1.3220 [ 1.3215; 1.3224] 1.7 1.5 HIC
## GERMANY 1.6877 [ 1.6872; 1.6881] 2.8 1.5 HIC
## GREECE 1.0272 [ 1.0262; 1.0283] 0.2 1.5 HIC
## HUNGARY 0.7149 [ 0.7140; 0.7157] 0.1 1.5 HIC
## INDIA 0.0211 [ 0.0211; 0.0212] 0.6 1.5 LMIC
## IRELAND 1.1315 [ 1.1300; 1.1331] 0.1 1.5 HIC
## ITALY 0.5573 [ 0.5569; 0.5576] 0.7 1.5 HIC
## JAPAN 0.0910 [ 0.0909; 0.0910] 0.2 1.5 HIC
## JORDAN 0.0856 [ 0.0852; 0.0859] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0254 [ 0.0253; 0.0255] 0.0 1.5 UMIC
## KUWAIT 0.4524 [ 0.4513; 0.4535] 0.0 1.5 HIC
## LATVIA 1.7258 [ 1.7228; 1.7288] 0.1 1.5 HIC
## LEBANON 0.4366 [ 0.4357; 0.4374] 0.1 1.5 UMIC
## LITHUANIA 0.4294 [ 0.4282; 0.4307] 0.0 1.5 HIC
## LUXEMBOURG 0.6676 [ 0.6641; 0.6710] 0.0 1.5 HIC
## MEXICO 0.0878 [ 0.0877; 0.0879] 0.2 1.5 UMIC
## MOROCCO 0.0514 [ 0.0512; 0.0515] 0.0 1.5 LMIC
## NETHERLANDS 0.7640 [ 0.7633; 0.7647] 0.3 1.5 HIC
## NEW ZEALAND 3.3486 [ 3.3459; 3.3514] 0.3 1.5 HIC
## NORWAY 2.7479 [ 2.7456; 2.7503] 0.3 1.5 HIC
## PAKISTAN 0.0364 [ 0.0364; 0.0365] 0.1 1.5 LMIC
## PERU 0.0566 [ 0.0565; 0.0568] 0.0 1.5 UMIC
## PHILIPPINES 0.0275 [ 0.0274; 0.0275] 0.1 1.5 LMIC
## POLAND 0.4448 [ 0.4445; 0.4452] 0.3 1.5 HIC
## PORTUGAL 1.3466 [ 1.3455; 1.3478] 0.3 1.5 HIC
## PUERTO RICO 13.2279 [13.2214; 13.2344] 0.9 1.5 HIC
## ROMANIA 1.1118 [ 1.1110; 1.1125] 0.4 1.5 UMIC
## RUSSIA 0.0930 [ 0.0930; 0.0931] 0.3 1.5 UMIC
## SAUDI ARABIA 0.4083 [ 0.4079; 0.4087] 0.3 1.5 HIC
## SERBIA 0.1056 [ 0.1052; 0.1060] 0.0 1.5 UMIC
## SLOVAKIA 1.4980 [ 1.4963; 1.4997] 0.2 1.5 HIC
## SLOVENIA 0.3248 [ 0.3235; 0.3261] 0.0 1.5 HIC
## SOUTH AFRICA 0.0642 [ 0.0640; 0.0643] 0.1 1.5 UMIC
## SOUTH KOREA 0.8851 [ 0.8847; 0.8855] 0.9 1.5 HIC
## SPAIN 2.0645 [ 2.0638; 2.0652] 1.9 1.5 HIC
## SWEDEN 3.4050 [ 3.4031; 3.4069] 0.7 1.5 HIC
## SWITZERLAND 0.5020 [ 0.5012; 0.5028] 0.1 1.5 HIC
## TAIWAN 0.2339 [ 0.2335; 0.2342] 0.1 1.5 HIC
## THAILAND 0.5326 [ 0.5324; 0.5329] 0.7 1.5 UMIC
## TUNISIA 0.0939 [ 0.0936; 0.0942] 0.0 1.5 LMIC
## TÜRKIYE 2.4956 [ 2.4950; 2.4961] 4.0 1.5 UMIC
## UNITED ARAB EMIRATES 0.0726 [ 0.0723; 0.0729] 0.0 1.5 HIC
## UNITED KINGDOM 5.7608 [ 5.7598; 5.7617] 7.6 1.5 HIC
## UNITED STATES 10.2970 [10.2964; 10.2975] 66.5 1.5 HIC
## URUGUAY 0.3608 [ 0.3598; 0.3619] 0.0 1.5 HIC
## VENEZUELA 0.0491 [ 0.0490; 0.0493] 0.0 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.6449 [5.6446; 5.6451] 73972.31 0
## Random effects model 0.3564 [0.2419; 0.5250] -5.22 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.5391 [1.9112; 6.1269]; tau = 1.5934 [1.3825; 2.4753]
## I^2 = 100.0%; H = 6297.32
##
## Test of heterogeneity:
## Q d.f. p-value
## 2537997217.08 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 7.0825 [7.0821; 7.0828] 996261547.11 100.0%
## income = UMIC 21 0.7007 [0.7006; 0.7009] 365220741.12 100.0%
## income = LMIC 6 0.0414 [0.0414; 0.0415] 14722177.24 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1161792751.61 2 0
## Within groups 1376204465.47 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.8986 [0.6263; 1.2893] 1.2893 1.1355
## income = UMIC 21 0.1170 [0.0495; 0.2767] 4.0459 2.0115
## income = LMIC 6 0.0502 [0.0217; 0.1160] 1.0951 1.0465
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 49.82 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.1275 [ 0.1273; 0.1277] 0.1 1.5 UMIC
## ARGENTINA 0.1241 [ 0.1240; 0.1243] 0.1 1.5 UMIC
## AUSTRALIA 0.6583 [ 0.6578; 0.6589] 0.3 1.5 HIC
## AUSTRIA 2.0880 [ 2.0864; 2.0896] 0.3 1.5 HIC
## BELARUS 0.0843 [ 0.0840; 0.0846] 0.0 1.5 UMIC
## BELGIUM 0.9062 [ 0.9053; 0.9071] 0.2 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.3009 [ 0.3000; 0.3019] 0.0 1.5 UMIC
## BRAZIL 0.1148 [ 0.1148; 0.1149] 0.4 1.5 UMIC
## BULGARIA 0.5224 [ 0.5215; 0.5233] 0.1 1.5 UMIC
## CANADA 4.7693 [ 4.7681; 4.7705] 3.2 1.5 HIC
## CHILE 0.0198 [ 0.0197; 0.0200] 0.0 1.5 HIC
## CHINA 0.0293 [ 0.0293; 0.0293] 0.8 1.5 UMIC
## COLOMBIA 0.0119 [ 0.0118; 0.0119] 0.0 1.5 UMIC
## CROATIA 0.1282 [ 0.1276; 0.1288] 0.0 1.5 HIC
## CZECH REPUBLIC 2.0591 [ 2.0577; 2.0606] 0.4 1.5 HIC
## ECUADOR 0.0520 [ 0.0519; 0.0522] 0.0 1.5 UMIC
## EGYPT 0.1489 [ 0.1488; 0.1491] 0.3 1.5 LMIC
## ESTONIA 1.1053 [ 1.1024; 1.1083] 0.0 1.5 HIC
## FINLAND 2.6654 [ 2.6631; 2.6676] 0.3 1.5 HIC
## FRANCE 1.3419 [ 1.3415; 1.3424] 1.6 1.5 HIC
## GERMANY 1.6769 [ 1.6765; 1.6774] 2.5 1.5 HIC
## GREECE 1.0026 [ 1.0016; 1.0036] 0.2 1.5 HIC
## HUNGARY 0.7477 [ 0.7468; 0.7486] 0.1 1.5 HIC
## INDIA 0.0231 [ 0.0231; 0.0232] 0.6 1.5 LMIC
## IRELAND 1.1919 [ 1.1903; 1.1936] 0.1 1.5 HIC
## ITALY 0.5694 [ 0.5691; 0.5698] 0.6 1.5 HIC
## JAPAN 0.0856 [ 0.0855; 0.0857] 0.2 1.5 HIC
## JORDAN 0.1192 [ 0.1189; 0.1196] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0230 [ 0.0229; 0.0231] 0.0 1.5 UMIC
## KUWAIT 0.9078 [ 0.9062; 0.9093] 0.1 1.5 HIC
## LATVIA 2.0008 [ 1.9975; 2.0041] 0.1 1.5 HIC
## LEBANON 0.4970 [ 0.4962; 0.4979] 0.1 1.5 UMIC
## LITHUANIA 0.5211 [ 0.5197; 0.5225] 0.0 1.5 HIC
## LUXEMBOURG 0.6295 [ 0.6261; 0.6328] 0.0 1.5 HIC
## MEXICO 0.0978 [ 0.0977; 0.0979] 0.2 1.5 UMIC
## MOROCCO 0.0369 [ 0.0368; 0.0370] 0.0 1.5 LMIC
## NETHERLANDS 0.7887 [ 0.7880; 0.7894] 0.2 1.5 HIC
## NEW ZEALAND 3.7529 [ 3.7500; 3.7558] 0.3 1.5 HIC
## NORWAY 2.9508 [ 2.9484; 2.9532] 0.3 1.5 HIC
## PAKISTAN 0.0311 [ 0.0310; 0.0311] 0.1 1.5 LMIC
## PERU 0.0733 [ 0.0732; 0.0735] 0.0 1.5 UMIC
## PHILIPPINES 0.0243 [ 0.0242; 0.0243] 0.0 1.5 LMIC
## POLAND 0.4682 [ 0.4678; 0.4685] 0.3 1.5 HIC
## PORTUGAL 1.4084 [ 1.4072; 1.4096] 0.3 1.5 HIC
## PUERTO RICO 14.8281 [14.8211; 14.8351] 0.9 1.5 HIC
## ROMANIA 1.3080 [ 1.3072; 1.3089] 0.5 1.5 UMIC
## RUSSIA 0.1532 [ 0.1531; 0.1533] 0.4 1.5 UMIC
## SAUDI ARABIA 0.5669 [ 0.5665; 0.5673] 0.3 1.5 HIC
## SERBIA 0.1059 [ 0.1055; 0.1062] 0.0 1.5 UMIC
## SLOVAKIA 1.5627 [ 1.5609; 1.5644] 0.2 1.5 HIC
## SLOVENIA 0.3402 [ 0.3389; 0.3415] 0.0 1.5 HIC
## SOUTH AFRICA 0.0655 [ 0.0654; 0.0656] 0.1 1.5 UMIC
## SOUTH KOREA 0.9343 [ 0.9339; 0.9348] 0.9 1.5 HIC
## SPAIN 2.1655 [ 2.1648; 2.1662] 1.8 1.5 HIC
## SWEDEN 3.9049 [ 3.9029; 3.9070] 0.7 1.5 HIC
## SWITZERLAND 0.5067 [ 0.5059; 0.5075] 0.1 1.5 HIC
## TAIWAN 0.2404 [ 0.2401; 0.2407] 0.1 1.5 HIC
## THAILAND 0.6236 [ 0.6233; 0.6239] 0.8 1.5 UMIC
## TUNISIA 0.0915 [ 0.0912; 0.0918] 0.0 1.5 LMIC
## TÜRKIYE 2.5773 [ 2.5767; 2.5779] 3.8 1.5 UMIC
## UNITED ARAB EMIRATES 0.0593 [ 0.0590; 0.0595] 0.0 1.5 HIC
## UNITED KINGDOM 6.1083 [ 6.1073; 6.1093] 7.4 1.5 HIC
## UNITED STATES 11.4514 [11.4508; 11.4520] 67.5 1.5 HIC
## URUGUAY 0.3376 [ 0.3365; 0.3386] 0.0 1.5 HIC
## VENEZUELA 0.0634 [ 0.0633; 0.0636] 0.0 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.2513 [6.2510; 6.2515] 82240.06 0
## Random effects model 0.3841 [0.2583; 0.5714] -4.72 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.6674 [1.9187; 6.1656]; tau = 1.6332 [1.3852; 2.4831]
## I^2 = 100.0%; H = 6695.71
##
## Test of heterogeneity:
## Q d.f. p-value
## 2869284786.03 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 7.9048 [7.9044; 7.9051] 1115960133.86 100.0%
## income = UMIC 21 0.6980 [0.6979; 0.6981] 390535736.08 100.0%
## income = LMIC 6 0.0398 [0.0398; 0.0399] 13102002.31 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1349686913.78 2 0
## Within groups 1519597872.25 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.9567 [0.6606; 1.3855] 1.3568 1.1648
## income = UMIC 21 0.1356 [0.0596; 0.3088] 3.6993 1.9234
## income = LMIC 6 0.0454 [0.0203; 0.1018] 1.0175 1.0087
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 55.09 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 1
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0899 [ 0.0898; 0.0901] 0.1 1.5 UMIC
## ARGENTINA 0.1258 [ 0.1257; 0.1260] 0.1 1.5 UMIC
## AUSTRALIA 0.7003 [ 0.6998; 0.7009] 0.3 1.5 HIC
## AUSTRIA 2.0631 [ 2.0615; 2.0646] 0.3 1.5 HIC
## BELARUS 0.1204 [ 0.1200; 0.1208] 0.0 1.5 UMIC
## BELGIUM 0.8770 [ 0.8761; 0.8779] 0.2 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.3650 [ 0.3640; 0.3661] 0.0 1.5 UMIC
## BRAZIL 0.1201 [ 0.1201; 0.1202] 0.4 1.5 UMIC
## BULGARIA 0.4317 [ 0.4309; 0.4325] 0.1 1.5 UMIC
## CANADA 4.8245 [ 4.8233; 4.8256] 3.2 1.5 HIC
## CHILE 0.0214 [ 0.0213; 0.0215] 0.0 1.5 HIC
## CHINA 0.0330 [ 0.0329; 0.0330] 0.8 1.5 UMIC
## COLOMBIA 0.0146 [ 0.0145; 0.0146] 0.0 1.5 UMIC
## CROATIA 0.1217 [ 0.1211; 0.1223] 0.0 1.5 HIC
## CZECH REPUBLIC 2.0989 [ 2.0974; 2.1003] 0.4 1.5 HIC
## ECUADOR 0.0512 [ 0.0510; 0.0513] 0.0 1.5 UMIC
## EGYPT 0.1647 [ 0.1646; 0.1648] 0.3 1.5 LMIC
## ESTONIA 1.2831 [ 1.2799; 1.2862] 0.0 1.5 HIC
## FINLAND 3.0971 [ 3.0947; 3.0996] 0.3 1.5 HIC
## FRANCE 1.3516 [ 1.3511; 1.3521] 1.5 1.5 HIC
## GERMANY 1.6750 [ 1.6746; 1.6755] 2.5 1.5 HIC
## GREECE 0.9888 [ 0.9878; 0.9898] 0.2 1.5 HIC
## HUNGARY 0.8107 [ 0.8098; 0.8117] 0.1 1.5 HIC
## INDIA 0.0244 [ 0.0244; 0.0245] 0.6 1.5 LMIC
## IRELAND 1.2545 [ 1.2528; 1.2562] 0.1 1.5 HIC
## ITALY 0.5734 [ 0.5731; 0.5737] 0.6 1.5 HIC
## JAPAN 0.0833 [ 0.0832; 0.0833] 0.2 1.5 HIC
## JORDAN 0.3329 [ 0.3323; 0.3335] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0263 [ 0.0262; 0.0265] 0.0 1.5 UMIC
## KUWAIT 0.9165 [ 0.9150; 0.9180] 0.1 1.5 HIC
## LATVIA 2.3533 [ 2.3498; 2.3569] 0.1 1.5 HIC
## LEBANON 0.4987 [ 0.4978; 0.4996] 0.1 1.5 UMIC
## LITHUANIA 0.5957 [ 0.5942; 0.5972] 0.0 1.5 HIC
## LUXEMBOURG 0.6434 [ 0.6400; 0.6467] 0.0 1.5 HIC
## MEXICO 0.1002 [ 0.1001; 0.1003] 0.2 1.5 UMIC
## MOROCCO 0.0402 [ 0.0401; 0.0403] 0.0 1.5 LMIC
## NETHERLANDS 0.8129 [ 0.8122; 0.8136] 0.2 1.5 HIC
## NEW ZEALAND 3.9910 [ 3.9880; 3.9939] 0.3 1.5 HIC
## NORWAY 3.1948 [ 3.1923; 3.1973] 0.3 1.5 HIC
## PAKISTAN 0.0328 [ 0.0328; 0.0328] 0.1 1.5 LMIC
## PERU 0.0703 [ 0.0701; 0.0704] 0.0 1.5 UMIC
## PHILIPPINES 0.0299 [ 0.0298; 0.0299] 0.1 1.5 LMIC
## POLAND 0.5082 [ 0.5079; 0.5086] 0.3 1.5 HIC
## PORTUGAL 1.4806 [ 1.4794; 1.4819] 0.3 1.5 HIC
## PUERTO RICO 13.9247 [13.9178; 13.9317] 0.7 1.5 HIC
## ROMANIA 1.3918 [ 1.3909; 1.3926] 0.5 1.5 UMIC
## RUSSIA 0.2102 [ 0.2101; 0.2103] 0.5 1.5 UMIC
## SAUDI ARABIA 0.7810 [ 0.7805; 0.7815] 0.5 1.5 HIC
## SERBIA 0.1066 [ 0.1063; 0.1070] 0.0 1.5 UMIC
## SLOVAKIA 1.7118 [ 1.7100; 1.7136] 0.2 1.5 HIC
## SLOVENIA 0.3713 [ 0.3700; 0.3727] 0.0 1.5 HIC
## SOUTH AFRICA 0.0662 [ 0.0661; 0.0663] 0.1 1.5 UMIC
## SOUTH KOREA 0.9307 [ 0.9303; 0.9312] 0.8 1.5 HIC
## SPAIN 2.2219 [ 2.2212; 2.2226] 1.8 1.5 HIC
## SWEDEN 4.4354 [ 4.4332; 4.4376] 0.8 1.5 HIC
## SWITZERLAND 0.5148 [ 0.5141; 0.5156] 0.1 1.5 HIC
## TAIWAN 0.2588 [ 0.2585; 0.2592] 0.1 1.5 HIC
## THAILAND 0.7063 [ 0.7060; 0.7067] 0.9 1.5 UMIC
## TUNISIA 0.0909 [ 0.0906; 0.0911] 0.0 1.5 LMIC
## TÜRKIYE 2.5175 [ 2.5170; 2.5181] 3.7 1.5 UMIC
## UNITED ARAB EMIRATES 0.0566 [ 0.0563; 0.0568] 0.0 1.5 HIC
## UNITED KINGDOM 6.1017 [ 6.1008; 6.1027] 7.2 1.5 HIC
## UNITED STATES 11.6873 [11.6867; 11.6879] 67.4 1.5 HIC
## URUGUAY 0.3050 [ 0.3040; 0.3059] 0.0 1.5 HIC
## VENEZUELA 0.0258 [ 0.0257; 0.0259] 0.0 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.2928 [6.2926; 6.2931] 83704.27 0
## Random effects model 0.4039 [0.2708; 0.6026] -4.44 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 2.7063 [1.8873; 6.0213]; tau = 1.6451 [1.3738; 2.4538]
## I^2 = 100.0%; H = 6850.89
##
## Test of heterogeneity:
## Q d.f. p-value
## 3003817304.32 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 8.0400 [8.0397; 8.0404] 1142563272.29 100.0%
## income = UMIC 21 0.6752 [0.6751; 0.6753] 389940176.39 100.0%
## income = LMIC 6 0.0435 [0.0435; 0.0435] 15082723.45 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1456231132.20 2 0
## Within groups 1547586172.13 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.9992 [0.6900; 1.4469] 1.3563 1.1646
## income = UMIC 21 0.1431 [0.0650; 0.3149] 3.4025 1.8446
## income = LMIC 6 0.0493 [0.0217; 0.1124] 1.0582 1.0287
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 53.45 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0709 [0.0708; 0.0711] 0.2 1.8 UMIC
## ARGENTINA 0.1063 [0.1061; 0.1065] 0.3 1.8 UMIC
## AUSTRALIA 0.5278 [0.5272; 0.5283] 0.7 1.8 HIC
## AUSTRIA 0.7538 [0.7528; 0.7548] 0.4 1.8 HIC
## BELARUS 0.0007 [0.0007; 0.0008] 0.0 1.8 UMIC
## BELGIUM 0.9523 [0.9513; 0.9533] 0.7 1.8 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0000 0.0 0.0 UMIC
## BULGARIA 0.0186 [0.0185; 0.0188] 0.0 1.8 UMIC
## CANADA 1.6387 [1.6380; 1.6395] 3.6 1.8 HIC
## CHILE 0.1689 [0.1685; 0.1692] 0.2 1.8 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0536 [0.0535; 0.0537] 0.2 1.8 UMIC
## CROATIA 0.0233 [0.0231; 0.0236] 0.0 1.8 HIC
## CZECH REPUBLIC 0.4263 [0.4256; 0.4269] 0.3 1.8 HIC
## ECUADOR 0.1220 [0.1217; 0.1223] 0.1 1.8 UMIC
## EGYPT 0.0305 [0.0305; 0.0306] 0.2 1.8 LMIC
## ESTONIA 0.1266 [0.1256; 0.1276] 0.0 1.8 HIC
## FINLAND 3.3965 [3.3939; 3.3991] 1.2 1.8 HIC
## FRANCE 2.1673 [2.1667; 2.1679] 8.8 1.8 HIC
## GERMANY 1.5554 [1.5549; 1.5558] 8.3 1.8 HIC
## GREECE 1.4149 [1.4138; 1.4161] 1.0 1.8 HIC
## HUNGARY 0.4953 [0.4946; 0.4961] 0.3 1.8 HIC
## INDIA 0.0441 [0.0441; 0.0441] 3.5 1.8 LMIC
## IRELAND 1.9534 [1.9512; 1.9556] 0.6 1.8 HIC
## ITALY 0.9209 [0.9205; 0.9213] 3.6 1.8 HIC
## JAPAN 0.0000 0.0 0.0 HIC
## JORDAN 0.0510 [0.0507; 0.0513] 0.0 1.8 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.0877 [0.0871; 0.0883] 0.0 1.8 HIC
## LATVIA 0.0843 [0.0837; 0.0849] 0.0 1.8 HIC
## LEBANON 0.1605 [0.1599; 0.1611] 0.1 1.8 UMIC
## LITHUANIA 0.1282 [0.1276; 0.1289] 0.0 1.8 HIC
## LUXEMBOURG 2.3963 [2.3891; 2.4035] 0.1 1.8 HIC
## MEXICO 0.2075 [0.2074; 0.2077] 1.5 1.8 UMIC
## MOROCCO 0.0162 [0.0161; 0.0163] 0.0 1.8 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0120 [0.0118; 0.0121] 0.0 1.8 HIC
## NORWAY 2.3244 [2.3221; 2.3267] 0.7 1.8 HIC
## PAKISTAN 0.0431 [0.0430; 0.0431] 0.5 1.8 LMIC
## PERU 0.0255 [0.0254; 0.0256] 0.0 1.8 UMIC
## PHILIPPINES 0.0270 [0.0269; 0.0270] 0.2 1.8 LMIC
## POLAND 0.0043 [0.0043; 0.0043] 0.0 1.8 HIC
## PORTUGAL 1.8127 [1.8114; 1.8140] 1.3 1.8 HIC
## PUERTO RICO 1.2632 [1.2613; 1.2651] 0.3 1.8 HIC
## ROMANIA 0.2512 [0.2508; 0.2515] 0.3 1.8 UMIC
## RUSSIA 0.0137 [0.0137; 0.0138] 0.1 1.8 UMIC
## SAUDI ARABIA 0.2557 [0.2553; 0.2560] 0.4 1.8 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 0.8130 [0.8117; 0.8143] 0.3 1.8 HIC
## SLOVENIA 1.0760 [1.0736; 1.0784] 0.1 1.8 HIC
## SOUTH AFRICA 0.0644 [0.0643; 0.0645] 0.2 1.8 UMIC
## SOUTH KOREA 0.3052 [0.3049; 0.3055] 1.0 1.8 HIC
## SPAIN 2.0490 [2.0484; 2.0497] 6.2 1.8 HIC
## SWEDEN 2.1903 [2.1887; 2.1919] 1.3 1.8 HIC
## SWITZERLAND 1.3323 [1.3310; 1.3337] 0.7 1.8 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.0578 [0.0575; 0.0580] 0.0 1.8 LMIC
## TÜRKIYE 0.2052 [0.2050; 0.2053] 0.9 1.8 UMIC
## UNITED ARAB EMIRATES 0.2101 [0.2095; 0.2106] 0.1 1.8 HIC
## UNITED KINGDOM 1.3586 [1.3581; 1.3591] 5.5 1.8 HIC
## UNITED STATES 2.1871 [2.1868; 2.1873] 43.5 1.8 HIC
## URUGUAY 0.0582 [0.0578; 0.0586] 0.0 1.8 HIC
## VENEZUELA 0.2297 [0.2294; 0.2300] 0.4 1.8 UMIC
##
## Number of studies combined: k = 56
##
## rate 95%-CI z p-value
## Common effect model 1.3693 [1.3692; 1.3694] 7418.94 0
## Random effects model 0.2088 [0.1559; 0.2796] -10.50 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2456 [1.2763; 3.9522]; tau = 1.1161 [1.1297; 1.9880]
## I^2 = 100.0%; H = 3145.69
##
## Test of heterogeneity:
## Q d.f. p-value
## 544245166.16 55 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 35 1.7950 [1.7948; 1.7951] 87012755.52 100.0%
## income = UMIC 15 0.1544 [0.1543; 0.1544] 10163892.16 100.0%
## income = LMIC 6 0.0424 [0.0424; 0.0424] 504421.61 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 446564096.87 2 0
## Within groups 97681069.29 53 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 35 0.4929 [0.4204; 0.5779] 0.2304 0.4800
## income = UMIC 15 0.0583 [0.0406; 0.0838] 0.5124 0.7158
## income = LMIC 6 0.0337 [0.0277; 0.0410] 0.0599 0.2447
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 464.43 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.2753 [0.2750; 0.2756] 0.5 1.8 UMIC
## ARGENTINA 0.1721 [0.1719; 0.1723] 0.4 1.8 UMIC
## AUSTRALIA 0.7022 [0.7016; 0.7028] 0.9 1.8 HIC
## AUSTRIA 0.9630 [0.9619; 0.9641] 0.5 1.8 HIC
## BELARUS 0.0048 [0.0047; 0.0049] 0.0 1.8 UMIC
## BELGIUM 1.2113 [1.2103; 1.2124] 0.7 1.8 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0149 [0.0149; 0.0149] 0.2 1.8 UMIC
## BULGARIA 0.1057 [0.1053; 0.1061] 0.0 1.8 UMIC
## CANADA 2.1523 [2.1515; 2.1531] 4.1 1.8 HIC
## CHILE 0.1928 [0.1924; 0.1931] 0.2 1.8 HIC
## CHINA 0.0000 0.0 0.0 UMIC
## COLOMBIA 0.0553 [0.0552; 0.0554] 0.1 1.8 UMIC
## CROATIA 0.0632 [0.0628; 0.0636] 0.0 1.8 HIC
## CZECH REPUBLIC 0.5902 [0.5894; 0.5909] 0.3 1.8 HIC
## ECUADOR 0.1724 [0.1721; 0.1728] 0.1 1.8 UMIC
## EGYPT 0.0666 [0.0665; 0.0667] 0.3 1.8 LMIC
## ESTONIA 0.1453 [0.1442; 0.1463] 0.0 1.8 HIC
## FINLAND 4.0030 [4.0002; 4.0058] 1.2 1.8 HIC
## FRANCE 2.5553 [2.5547; 2.5560] 9.0 1.8 HIC
## GERMANY 1.8744 [1.8739; 1.8749] 8.5 1.8 HIC
## GREECE 1.8254 [1.8241; 1.8267] 1.1 1.8 HIC
## HUNGARY 0.5623 [0.5615; 0.5631] 0.3 1.8 HIC
## INDIA 0.0531 [0.0530; 0.0531] 3.6 1.8 LMIC
## IRELAND 2.6032 [2.6008; 2.6057] 0.7 1.8 HIC
## ITALY 1.1183 [1.1179; 1.1188] 3.7 1.8 HIC
## JAPAN 0.0000 0.0 0.0 HIC
## JORDAN 0.0665 [0.0662; 0.0669] 0.0 1.8 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1249 [0.1242; 0.1256] 0.0 1.8 HIC
## LATVIA 0.1286 [0.1278; 0.1294] 0.0 1.8 HIC
## LEBANON 0.2201 [0.2194; 0.2208] 0.1 1.8 UMIC
## LITHUANIA 0.1815 [0.1807; 0.1823] 0.0 1.8 HIC
## LUXEMBOURG 2.6071 [2.5997; 2.6145] 0.1 1.8 HIC
## MEXICO 0.2079 [0.2077; 0.2080] 1.3 1.8 UMIC
## MOROCCO 0.0335 [0.0334; 0.0336] 0.1 1.8 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0279 [0.0276; 0.0281] 0.0 1.8 HIC
## NORWAY 2.1852 [2.1830; 2.1873] 0.6 1.8 HIC
## PAKISTAN 0.0910 [0.0909; 0.0911] 0.9 1.8 LMIC
## PERU 0.0375 [0.0374; 0.0376] 0.1 1.8 UMIC
## PHILIPPINES 0.0372 [0.0371; 0.0372] 0.2 1.8 LMIC
## POLAND 0.0071 [0.0070; 0.0071] 0.0 1.8 HIC
## PORTUGAL 2.2539 [2.2525; 2.2554] 1.3 1.8 HIC
## PUERTO RICO 1.2524 [1.2505; 1.2543] 0.3 1.8 HIC
## ROMANIA 0.4295 [0.4290; 0.4300] 0.5 1.8 UMIC
## RUSSIA 0.0284 [0.0284; 0.0285] 0.2 1.8 UMIC
## SAUDI ARABIA 0.3084 [0.3081; 0.3088] 0.5 1.8 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.1281 [1.1266; 1.1296] 0.3 1.8 HIC
## SLOVENIA 1.3641 [1.3615; 1.3668] 0.2 1.8 HIC
## SOUTH AFRICA 0.1130 [0.1129; 0.1132] 0.3 1.8 UMIC
## SOUTH KOREA 0.4698 [0.4694; 0.4701] 1.3 1.8 HIC
## SPAIN 2.6137 [2.6129; 2.6145] 6.8 1.8 HIC
## SWEDEN 2.7350 [2.7332; 2.7368] 1.4 1.8 HIC
## SWITZERLAND 1.5463 [1.5449; 1.5478] 0.7 1.8 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.1176 [0.1172; 0.1179] 0.1 1.8 LMIC
## TÜRKIYE 0.2955 [0.2953; 0.2957] 1.2 1.8 UMIC
## UNITED ARAB EMIRATES 0.3381 [0.3374; 0.3387] 0.2 1.8 HIC
## UNITED KINGDOM 1.8136 [1.8130; 1.8141] 6.4 1.8 HIC
## UNITED STATES 2.2201 [2.2199; 2.2204] 38.1 1.8 HIC
## URUGUAY 0.1756 [0.1748; 0.1763] 0.0 1.8 HIC
## VENEZUELA 0.2484 [0.2480; 0.2487] 0.4 1.8 UMIC
##
## Number of studies combined: k = 57
##
## rate 95%-CI z p-value
## Common effect model 1.4606 [1.4604; 1.4607] 9668.73 0
## Random effects model 0.2979 [0.2233; 0.3976] -8.23 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2346 [1.2416; 3.6478]; tau = 1.1111 [1.1143; 1.9099]
## I^2 = 100.0%; H = 3441.39
##
## Test of heterogeneity:
## Q d.f. p-value
## 663216284.22 56 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 35 1.9933 [1.9932; 1.9935] 88346534.61 100.0%
## income = UMIC 16 0.1888 [0.1887; 0.1888] 20128030.32 100.0%
## income = LMIC 6 0.0586 [0.0586; 0.0586] 1996551.67 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 552745167.61 2 0
## Within groups 110471116.61 54 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 35 0.6633 [0.5732; 0.7675] 0.1939 0.4404
## income = UMIC 16 0.0943 [0.0635; 0.1401] 0.6523 0.8076
## income = LMIC 6 0.0601 [0.0452; 0.0799] 0.1273 0.3567
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 262.35 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.3584 [0.3581; 0.3587] 0.6 1.7 UMIC
## ARGENTINA 0.2832 [0.2829; 0.2835] 0.6 1.7 UMIC
## AUSTRALIA 0.8531 [0.8524; 0.8537] 0.9 1.7 HIC
## AUSTRIA 1.2971 [1.2959; 1.2984] 0.5 1.7 HIC
## BELARUS 0.0057 [0.0056; 0.0058] 0.0 1.7 UMIC
## BELGIUM 1.7617 [1.7604; 1.7630] 0.9 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0598 [0.0597; 0.0598] 0.6 1.7 UMIC
## BULGARIA 0.1647 [0.1642; 0.1652] 0.1 1.7 UMIC
## CANADA 2.5654 [2.5645; 2.5663] 4.3 1.7 HIC
## CHILE 0.2310 [0.2307; 0.2314] 0.2 1.7 HIC
## CHINA 0.0000 [0.0000; 0.0000] 0.0 1.7 UMIC
## COLOMBIA 0.0525 [0.0524; 0.0526] 0.1 1.7 UMIC
## CROATIA 0.1874 [0.1868; 0.1881] 0.0 1.7 HIC
## CZECH REPUBLIC 0.7723 [0.7714; 0.7731] 0.4 1.7 HIC
## ECUADOR 0.2196 [0.2193; 0.2200] 0.2 1.7 UMIC
## EGYPT 0.1329 [0.1327; 0.1330] 0.5 1.7 LMIC
## ESTONIA 0.1949 [0.1936; 0.1961] 0.0 1.7 HIC
## FINLAND 4.4528 [4.4499; 4.4558] 1.2 1.7 HIC
## FRANCE 2.9223 [2.9216; 2.9230] 9.0 1.7 HIC
## GERMANY 2.1889 [2.1883; 2.1894] 8.7 1.7 HIC
## GREECE 2.0220 [2.0206; 2.0234] 1.1 1.7 HIC
## HUNGARY 0.6597 [0.6589; 0.6605] 0.3 1.7 HIC
## INDIA 0.0606 [0.0605; 0.0606] 3.7 1.7 LMIC
## IRELAND 3.0900 [3.0873; 3.0926] 0.7 1.7 HIC
## ITALY 1.3033 [1.3028; 1.3037] 3.8 1.7 HIC
## JAPAN 0.1340 [0.1339; 0.1341] 0.8 1.7 HIC
## JORDAN 0.0748 [0.0744; 0.0751] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1322 [0.1315; 0.1329] 0.0 1.7 HIC
## LATVIA 0.0932 [0.0925; 0.0939] 0.0 1.7 HIC
## LEBANON 0.2822 [0.2814; 0.2830] 0.1 1.7 UMIC
## LITHUANIA 0.2761 [0.2751; 0.2770] 0.0 1.7 HIC
## LUXEMBOURG 2.8977 [2.8900; 2.9055] 0.1 1.7 HIC
## MEXICO 0.2123 [0.2122; 0.2124] 1.2 1.7 UMIC
## MOROCCO 0.0372 [0.0371; 0.0373] 0.1 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.0433 [0.0429; 0.0436] 0.0 1.7 HIC
## NORWAY 2.1474 [2.1452; 2.1495] 0.5 1.7 HIC
## PAKISTAN 0.1127 [0.1127; 0.1128] 1.0 1.7 LMIC
## PERU 0.0528 [0.0527; 0.0530] 0.1 1.7 UMIC
## PHILIPPINES 0.0396 [0.0395; 0.0397] 0.2 1.7 LMIC
## POLAND 0.0090 [0.0089; 0.0090] 0.0 1.7 HIC
## PORTUGAL 2.6513 [2.6496; 2.6529] 1.4 1.7 HIC
## PUERTO RICO 1.2272 [1.2253; 1.2291] 0.2 1.7 HIC
## ROMANIA 0.3411 [0.3407; 0.3415] 0.3 1.7 UMIC
## RUSSIA 0.0725 [0.0724; 0.0725] 0.5 1.7 UMIC
## SAUDI ARABIA 0.3637 [0.3633; 0.3641] 0.5 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.4980 [1.4963; 1.4997] 0.4 1.7 HIC
## SLOVENIA 1.6411 [1.6382; 1.6440] 0.2 1.7 HIC
## SOUTH AFRICA 0.1369 [0.1367; 0.1371] 0.3 1.7 UMIC
## SOUTH KOREA 0.5801 [0.5797; 0.5804] 1.4 1.7 HIC
## SPAIN 3.0765 [3.0757; 3.0773] 7.1 1.7 HIC
## SWEDEN 3.0492 [3.0473; 3.0510] 1.4 1.7 HIC
## SWITZERLAND 1.8991 [1.8975; 1.9007] 0.7 1.7 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.0669 [0.0668; 0.0670] 0.2 1.7 UMIC
## TUNISIA 0.1599 [0.1595; 0.1603] 0.1 1.7 LMIC
## TÜRKIYE 0.3880 [0.3878; 0.3883] 1.4 1.7 UMIC
## UNITED ARAB EMIRATES 0.6148 [0.6140; 0.6157] 0.3 1.7 HIC
## UNITED KINGDOM 2.3866 [2.3860; 2.3873] 7.4 1.7 HIC
## UNITED STATES 2.1808 [2.1805; 2.1811] 33.1 1.7 HIC
## URUGUAY 0.3082 [0.3073; 0.3092] 0.1 1.7 HIC
## VENEZUELA 0.2893 [0.2890; 0.2896] 0.4 1.7 UMIC
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 1.4998 [1.4997; 1.4999] 11053.78 0
## Random effects model 0.3117 [0.2336; 0.4158] -7.93 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.2975 [1.2802; 3.5669]; tau = 1.1391 [1.1315; 1.8886]
## I^2 = 100.0%; H = 3745.29
##
## Test of heterogeneity:
## Q d.f. p-value
## 827603287.42 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 36 2.1150 [2.1148; 2.1151] 143362365.55 100.0%
## income = UMIC 18 0.2005 [0.2004; 0.2006] 24514517.79 100.0%
## income = LMIC 6 0.0728 [0.0728; 0.0728] 4876895.29 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 654849508.79 2 0
## Within groups 172753778.63 57 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 36 0.7848 [0.6626; 0.9296] 0.2685 0.5181
## income = UMIC 18 0.0782 [0.0554; 0.1105] 0.5575 0.7466
## income = LMIC 6 0.0773 [0.0527; 0.1135] 0.2298 0.4794
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 217.27 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5023 [0.5019; 0.5026] 0.8 1.6 UMIC
## ARGENTINA 0.4871 [0.4868; 0.4875] 0.8 1.6 UMIC
## AUSTRALIA 1.0422 [1.0415; 1.0429] 1.0 1.6 HIC
## AUSTRIA 1.6069 [1.6055; 1.6083] 0.6 1.6 HIC
## BELARUS 0.0049 [0.0049; 0.0050] 0.0 1.6 UMIC
## BELGIUM 2.1459 [2.1444; 2.1473] 1.0 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0047 [0.0046; 0.0048] 0.0 1.6 UMIC
## BRAZIL 0.0849 [0.0848; 0.0849] 0.7 1.6 UMIC
## BULGARIA 0.2681 [0.2675; 0.2687] 0.1 1.6 UMIC
## CANADA 2.8489 [2.8480; 2.8498] 4.1 1.6 HIC
## CHILE 0.2907 [0.2903; 0.2912] 0.2 1.6 HIC
## CHINA 0.0006 [0.0006; 0.0006] 0.0 1.6 UMIC
## COLOMBIA 0.0601 [0.0600; 0.0602] 0.1 1.6 UMIC
## CROATIA 0.2490 [0.2483; 0.2498] 0.0 1.6 HIC
## CZECH REPUBLIC 1.0325 [1.0315; 1.0335] 0.5 1.6 HIC
## ECUADOR 0.2650 [0.2645; 0.2654] 0.2 1.6 UMIC
## EGYPT 0.2101 [0.2100; 0.2103] 0.7 1.6 LMIC
## ESTONIA 0.2309 [0.2295; 0.2322] 0.0 1.6 HIC
## FINLAND 4.7455 [4.7425; 4.7485] 1.1 1.6 HIC
## FRANCE 3.1118 [3.1111; 3.1125] 8.2 1.6 HIC
## GERMANY 2.3903 [2.3897; 2.3909] 8.0 1.6 HIC
## GREECE 2.1845 [2.1831; 2.1860] 1.0 1.6 HIC
## HUNGARY 0.7808 [0.7799; 0.7817] 0.3 1.6 HIC
## INDIA 0.0673 [0.0672; 0.0673] 3.5 1.6 LMIC
## IRELAND 3.7947 [3.7917; 3.7976] 0.7 1.6 HIC
## ITALY 1.4936 [1.4931; 1.4942] 3.7 1.6 HIC
## JAPAN 0.9483 [0.9481; 0.9486] 5.1 1.6 HIC
## JORDAN 0.0969 [0.0965; 0.0972] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0096 [0.0095; 0.0097] 0.0 1.6 UMIC
## KUWAIT 0.1977 [0.1969; 0.1985] 0.0 1.6 HIC
## LATVIA 0.0698 [0.0692; 0.0703] 0.0 1.6 HIC
## LEBANON 0.4274 [0.4265; 0.4283] 0.1 1.6 UMIC
## LITHUANIA 0.4855 [0.4842; 0.4868] 0.1 1.6 HIC
## LUXEMBOURG 3.0533 [3.0455; 3.0612] 0.1 1.6 HIC
## MEXICO 0.1896 [0.1894; 0.1897] 0.9 1.6 UMIC
## MOROCCO 0.0429 [0.0428; 0.0430] 0.1 1.6 LMIC
## NETHERLANDS 1.8776 [1.8765; 1.8787] 1.3 1.6 HIC
## NEW ZEALAND 0.0559 [0.0555; 0.0562] 0.0 1.6 HIC
## NORWAY 2.4446 [2.4424; 2.4469] 0.5 1.6 HIC
## PAKISTAN 0.1482 [0.1481; 0.1483] 1.1 1.6 LMIC
## PERU 0.0621 [0.0619; 0.0622] 0.1 1.6 UMIC
## PHILIPPINES 0.0455 [0.0455; 0.0456] 0.2 1.6 LMIC
## POLAND 0.0169 [0.0169; 0.0170] 0.0 1.6 HIC
## PORTUGAL 2.7607 [2.7590; 2.7623] 1.2 1.6 HIC
## PUERTO RICO 1.2731 [1.2712; 1.2750] 0.2 1.6 HIC
## ROMANIA 0.3141 [0.3137; 0.3145] 0.3 1.6 UMIC
## RUSSIA 0.1415 [0.1414; 0.1416] 0.8 1.6 UMIC
## SAUDI ARABIA 0.4245 [0.4241; 0.4249] 0.5 1.6 HIC
## SERBIA 0.0287 [0.0285; 0.0289] 0.0 1.6 UMIC
## SLOVAKIA 2.0042 [2.0022; 2.0062] 0.5 1.6 HIC
## SLOVENIA 1.8618 [1.8587; 1.8649] 0.2 1.6 HIC
## SOUTH AFRICA 0.1591 [0.1589; 0.1592] 0.3 1.6 UMIC
## SOUTH KOREA 0.6952 [0.6948; 0.6956] 1.4 1.6 HIC
## SPAIN 3.5436 [3.5427; 3.5445] 6.9 1.6 HIC
## SWEDEN 3.2395 [3.2376; 3.2413] 1.3 1.6 HIC
## SWITZERLAND 2.1983 [2.1966; 2.2000] 0.7 1.6 HIC
## TAIWAN 0.0000 0.0 0.0 HIC
## THAILAND 0.1031 [0.1030; 0.1033] 0.3 1.6 UMIC
## TUNISIA 0.1955 [0.1950; 0.1959] 0.1 1.6 LMIC
## TÜRKIYE 0.5242 [0.5240; 0.5245] 1.6 1.6 UMIC
## UNITED ARAB EMIRATES 0.6845 [0.6836; 0.6854] 0.3 1.6 HIC
## UNITED KINGDOM 2.9403 [2.9396; 2.9409] 7.8 1.6 HIC
## UNITED STATES 2.1866 [2.1863; 2.1868] 28.3 1.6 HIC
## URUGUAY 0.3948 [0.3937; 0.3959] 0.1 1.6 HIC
## VENEZUELA 0.3710 [0.3706; 0.3713] 0.4 1.6 UMIC
##
## Number of studies combined: k = 64
##
## rate 95%-CI z p-value
## Common effect model 1.5664 [1.5663; 1.5665] 13307.14 0
## Random effects model 0.3546 [0.2720; 0.4623] -7.66 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1715 [1.2291; 3.2801]; tau = 1.0824 [1.1087; 1.8111]
## I^2 = 100.0%; H = 3807.60
##
## Test of heterogeneity:
## Q d.f. p-value
## 913361648.43 63 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 2.2143 [2.2142; 2.2145] 141815376.74 100.0%
## income = UMIC 21 0.2544 [0.2543; 0.2544] 43490994.08 100.0%
## income = LMIC 6 0.0912 [0.0911; 0.0912] 11211744.77 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 716843532.84 2 0
## Within groups 196518115.59 61 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 0.9960 [0.8568; 1.1578] 0.2183 0.4673
## income = UMIC 21 0.0834 [0.0579; 0.1202] 0.7308 0.8549
## income = LMIC 6 0.0963 [0.0582; 0.1595] 0.3969 0.6300
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 205.97 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5436 [0.5432; 0.5440] 0.7 1.5 UMIC
## ARGENTINA 0.6692 [0.6688; 0.6696] 1.0 1.5 UMIC
## AUSTRALIA 1.1969 [1.1962; 1.1977] 1.0 1.5 HIC
## AUSTRIA 1.9266 [1.9250; 1.9281] 0.6 1.5 HIC
## BELARUS 0.0061 [0.0060; 0.0062] 0.0 1.5 UMIC
## BELGIUM 2.2474 [2.2459; 2.2489] 0.9 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0084 [0.0082; 0.0085] 0.0 1.5 UMIC
## BRAZIL 0.1127 [0.1126; 0.1128] 0.8 1.5 UMIC
## BULGARIA 0.0647 [0.0644; 0.0650] 0.0 1.5 UMIC
## CANADA 3.1446 [3.1436; 3.1456] 4.0 1.5 HIC
## CHILE 0.3808 [0.3804; 0.3813] 0.2 1.5 HIC
## CHINA 0.0017 [0.0017; 0.0017] 0.1 1.5 UMIC
## COLOMBIA 0.0711 [0.0710; 0.0712] 0.1 1.5 UMIC
## CROATIA 0.3248 [0.3239; 0.3257] 0.1 1.5 HIC
## CZECH REPUBLIC 1.1044 [1.1034; 1.1055] 0.4 1.5 HIC
## ECUADOR 0.3125 [0.3120; 0.3129] 0.2 1.5 UMIC
## EGYPT 0.3476 [0.3474; 0.3478] 1.1 1.5 LMIC
## ESTONIA 0.2598 [0.2584; 0.2613] 0.0 1.5 HIC
## FINLAND 5.0579 [5.0548; 5.0610] 1.0 1.5 HIC
## FRANCE 3.4227 [3.4219; 3.4234] 8.0 1.5 HIC
## GERMANY 2.6294 [2.6288; 2.6300] 7.8 1.5 HIC
## GREECE 2.1222 [2.1208; 2.1237] 0.8 1.5 HIC
## HUNGARY 0.9178 [0.9168; 0.9188] 0.3 1.5 HIC
## INDIA 0.0698 [0.0698; 0.0699] 3.2 1.5 LMIC
## IRELAND 4.8273 [4.8240; 4.8306] 0.8 1.5 HIC
## ITALY 1.6178 [1.6172; 1.6183] 3.6 1.5 HIC
## JAPAN 1.6550 [1.6546; 1.6554] 7.8 1.5 HIC
## JORDAN 0.1713 [0.1709; 0.1718] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0219 [0.0218; 0.0220] 0.0 1.5 UMIC
## KUWAIT 0.3129 [0.3119; 0.3139] 0.0 1.5 HIC
## LATVIA 0.0739 [0.0733; 0.0745] 0.0 1.5 HIC
## LEBANON 0.4941 [0.4931; 0.4951] 0.1 1.5 UMIC
## LITHUANIA 0.5840 [0.5826; 0.5855] 0.1 1.5 HIC
## LUXEMBOURG 3.3108 [3.3027; 3.3189] 0.1 1.5 HIC
## MEXICO 0.1834 [0.1833; 0.1835] 0.8 1.5 UMIC
## MOROCCO 0.0481 [0.0479; 0.0482] 0.1 1.5 LMIC
## NETHERLANDS 2.1870 [2.1858; 2.1881] 1.3 1.5 HIC
## NEW ZEALAND 0.0606 [0.0602; 0.0610] 0.0 1.5 HIC
## NORWAY 2.6430 [2.6406; 2.6454] 0.5 1.5 HIC
## PAKISTAN 0.1655 [0.1654; 0.1656] 1.1 1.5 LMIC
## PERU 0.0764 [0.0762; 0.0766] 0.1 1.5 UMIC
## PHILIPPINES 0.0513 [0.0512; 0.0513] 0.2 1.5 LMIC
## POLAND 0.0124 [0.0124; 0.0125] 0.0 1.5 HIC
## PORTUGAL 3.1609 [3.1591; 3.1627] 1.2 1.5 HIC
## PUERTO RICO 1.2744 [1.2724; 1.2763] 0.2 1.5 HIC
## ROMANIA 0.2308 [0.2305; 0.2312] 0.2 1.5 UMIC
## RUSSIA 0.2983 [0.2982; 0.2985] 1.6 1.5 UMIC
## SAUDI ARABIA 0.6099 [0.6094; 0.6103] 0.7 1.5 HIC
## SERBIA 0.0514 [0.0512; 0.0517] 0.0 1.5 UMIC
## SLOVAKIA 2.1634 [2.1614; 2.1655] 0.4 1.5 HIC
## SLOVENIA 2.0907 [2.0875; 2.0940] 0.2 1.5 HIC
## SOUTH AFRICA 0.1847 [0.1845; 0.1849] 0.4 1.5 UMIC
## SOUTH KOREA 1.0076 [1.0072; 1.0081] 1.9 1.5 HIC
## SPAIN 3.9095 [3.9085; 3.9104] 6.7 1.5 HIC
## SWEDEN 3.2022 [3.2003; 3.2040] 1.1 1.5 HIC
## SWITZERLAND 2.4428 [2.4410; 2.4445] 0.7 1.5 HIC
## TAIWAN 0.0082 [0.0082; 0.0083] 0.0 1.5 HIC
## THAILAND 0.1173 [0.1172; 0.1175] 0.3 1.5 UMIC
## TUNISIA 0.2953 [0.2948; 0.2959] 0.1 1.5 LMIC
## TÜRKIYE 0.7695 [0.7692; 0.7699] 2.1 1.5 UMIC
## UNITED ARAB EMIRATES 0.2766 [0.2760; 0.2772] 0.1 1.5 HIC
## UNITED KINGDOM 3.4816 [3.4808; 3.4823] 8.2 1.5 HIC
## UNITED STATES 2.0969 [2.0967; 2.0972] 24.2 1.5 HIC
## URUGUAY 0.6758 [0.6743; 0.6772] 0.1 1.5 HIC
## VENEZUELA 0.4633 [0.4629; 0.4637] 0.5 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.6762 [1.6761; 1.6763] 16299.08 0
## Random effects model 0.3943 [0.3052; 0.5095] -7.12 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.1102 [1.2216; 3.2212]; tau = 1.0537 [1.1053; 1.7948]
## I^2 = 100.0%; H = 3953.76
##
## Test of heterogeneity:
## Q d.f. p-value
## 1000461830.41 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 2.3945 [2.3944; 2.3947] 139373093.33 100.0%
## income = UMIC 21 0.3334 [0.3333; 0.3334] 71090434.70 100.0%
## income = LMIC 6 0.1136 [0.1135; 0.1136] 25617580.11 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 764380722.27 2 0
## Within groups 236081108.14 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 0.9818 [0.8552; 1.1272] 0.1886 0.4343
## income = UMIC 21 0.1064 [0.0709; 0.1598] 0.9027 0.9501
## income = LMIC 6 0.1196 [0.0608; 0.2351] 0.7143 0.8451
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 131.43 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.6123 [0.6119; 0.6128] 0.7 1.5 UMIC
## ARGENTINA 0.8307 [0.8302; 0.8311] 1.1 1.5 UMIC
## AUSTRALIA 3.1224 [3.1212; 3.1236] 2.3 1.5 HIC
## AUSTRIA 2.1375 [2.1358; 2.1391] 0.6 1.5 HIC
## BELARUS 0.0123 [0.0122; 0.0124] 0.0 1.5 UMIC
## BELGIUM 2.0611 [2.0597; 2.0625] 0.7 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0126 [0.0124; 0.0128] 0.0 1.5 UMIC
## BRAZIL 0.1486 [0.1485; 0.1487] 0.9 1.5 UMIC
## BULGARIA 0.0809 [0.0806; 0.0813] 0.0 1.5 UMIC
## CANADA 3.7305 [3.7294; 3.7315] 4.1 1.5 HIC
## CHILE 0.4682 [0.4676; 0.4687] 0.3 1.5 HIC
## CHINA 0.0024 [0.0024; 0.0024] 0.1 1.5 UMIC
## COLOMBIA 0.0892 [0.0891; 0.0893] 0.1 1.5 UMIC
## CROATIA 0.3720 [0.3710; 0.3730] 0.0 1.5 HIC
## CZECH REPUBLIC 1.3551 [1.3539; 1.3562] 0.4 1.5 HIC
## ECUADOR 0.3316 [0.3311; 0.3320] 0.2 1.5 UMIC
## EGYPT 0.4690 [0.4688; 0.4693] 1.3 1.5 LMIC
## ESTONIA 0.3397 [0.3381; 0.3414] 0.0 1.5 HIC
## FINLAND 5.3754 [5.3721; 5.3786] 0.9 1.5 HIC
## FRANCE 3.6856 [3.6849; 3.6864] 7.4 1.5 HIC
## GERMANY 2.8213 [2.8207; 2.8219] 7.2 1.5 HIC
## GREECE 2.3245 [2.3230; 2.3260] 0.8 1.5 HIC
## HUNGARY 1.0685 [1.0674; 1.0696] 0.3 1.5 HIC
## INDIA 0.0717 [0.0717; 0.0718] 2.9 1.5 LMIC
## IRELAND 6.0138 [6.0100; 6.0175] 0.9 1.5 HIC
## ITALY 1.7605 [1.7600; 1.7611] 3.3 1.5 HIC
## JAPAN 2.1922 [2.1917; 2.1926] 8.8 1.5 HIC
## JORDAN 0.3112 [0.3106; 0.3118] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0286 [0.0284; 0.0287] 0.0 1.5 UMIC
## KUWAIT 0.6898 [0.6884; 0.6913] 0.1 1.5 HIC
## LATVIA 0.0938 [0.0931; 0.0945] 0.0 1.5 HIC
## LEBANON 0.5122 [0.5112; 0.5131] 0.1 1.5 UMIC
## LITHUANIA 0.7074 [0.7058; 0.7089] 0.1 1.5 HIC
## LUXEMBOURG 3.3751 [3.3670; 3.3832] 0.1 1.5 HIC
## MEXICO 0.1822 [0.1821; 0.1823] 0.7 1.5 UMIC
## MOROCCO 0.0512 [0.0510; 0.0513] 0.1 1.5 LMIC
## NETHERLANDS 2.4227 [2.4214; 2.4239] 1.3 1.5 HIC
## NEW ZEALAND 0.0756 [0.0752; 0.0760] 0.0 1.5 HIC
## NORWAY 2.7219 [2.7196; 2.7243] 0.4 1.5 HIC
## PAKISTAN 0.1842 [0.1841; 0.1843] 1.1 1.5 LMIC
## PERU 0.0920 [0.0918; 0.0922] 0.1 1.5 UMIC
## PHILIPPINES 0.0583 [0.0583; 0.0584] 0.2 1.5 LMIC
## POLAND 0.0159 [0.0159; 0.0160] 0.0 1.5 HIC
## PORTUGAL 3.1361 [3.1343; 3.1379] 1.0 1.5 HIC
## PUERTO RICO 1.2648 [1.2629; 1.2668] 0.1 1.5 HIC
## ROMANIA 0.1968 [0.1965; 0.1972] 0.1 1.5 UMIC
## RUSSIA 0.6995 [0.6993; 0.6997] 3.2 1.5 UMIC
## SAUDI ARABIA 0.9389 [0.9383; 0.9395] 0.9 1.5 HIC
## SERBIA 0.1583 [0.1578; 0.1587] 0.0 1.5 UMIC
## SLOVAKIA 2.2623 [2.2602; 2.2644] 0.4 1.5 HIC
## SLOVENIA 2.2580 [2.2546; 2.2614] 0.1 1.5 HIC
## SOUTH AFRICA 0.1957 [0.1955; 0.1959] 0.3 1.5 UMIC
## SOUTH KOREA 1.1367 [1.1362; 1.1372] 1.8 1.5 HIC
## SPAIN 4.2439 [4.2429; 4.2448] 6.2 1.5 HIC
## SWEDEN 3.2839 [3.2820; 3.2858] 1.0 1.5 HIC
## SWITZERLAND 2.6708 [2.6689; 2.6726] 0.7 1.5 HIC
## TAIWAN 0.0939 [0.0937; 0.0941] 0.1 1.5 HIC
## THAILAND 0.1231 [0.1230; 0.1233] 0.3 1.5 UMIC
## TUNISIA 0.3557 [0.3552; 0.3563] 0.1 1.5 LMIC
## TÜRKIYE 1.0282 [1.0278; 1.0286] 2.4 1.5 UMIC
## UNITED ARAB EMIRATES 0.4835 [0.4828; 0.4843] 0.1 1.5 HIC
## UNITED KINGDOM 4.2010 [4.2002; 4.2019] 8.6 1.5 HIC
## UNITED STATES 2.2311 [2.2308; 2.2313] 22.1 1.5 HIC
## URUGUAY 0.7672 [0.7657; 0.7688] 0.1 1.5 HIC
## VENEZUELA 0.6107 [0.6102; 0.6111] 0.6 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 1.8841 [1.8840; 1.8842] 21624.21 0
## Random effects model 0.5042 [0.3934; 0.6462] -5.41 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0417 [1.1631; 3.0301]; tau = 1.0207 [1.0785; 1.7407]
## I^2 = 100.0%; H = 4163.34
##
## Test of heterogeneity:
## Q d.f. p-value
## 1109335495.37 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 2.6903 [2.6902; 2.6905] 155938952.31 100.0%
## income = UMIC 21 0.4937 [0.4936; 0.4938] 103644354.42 100.0%
## income = LMIC 6 0.1363 [0.1363; 0.1364] 41553714.39 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 808198474.25 2 0
## Within groups 301137021.12 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.2461 [1.0878; 1.4274] 0.1826 0.4273
## income = UMIC 21 0.1424 [0.0937; 0.2163] 0.9551 0.9773
## income = LMIC 6 0.1369 [0.0620; 0.3020] 0.9781 0.9890
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 117.51 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.8746 [0.8741; 0.8750] 0.9 1.5 UMIC
## ARGENTINA 0.9517 [0.9512; 0.9522] 1.1 1.5 UMIC
## AUSTRALIA 5.4618 [5.4602; 5.4633] 3.5 1.5 HIC
## AUSTRIA 2.3277 [2.3261; 2.3294] 0.5 1.5 HIC
## BELARUS 0.0146 [0.0145; 0.0147] 0.0 1.5 UMIC
## BELGIUM 2.0963 [2.0949; 2.0978] 0.6 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0180 [0.0177; 0.0182] 0.0 1.5 UMIC
## BRAZIL 0.1909 [0.1908; 0.1910] 1.1 1.5 UMIC
## BULGARIA 0.1039 [0.1035; 0.1043] 0.0 1.5 UMIC
## CANADA 4.2330 [4.2318; 4.2341] 4.1 1.5 HIC
## CHILE 0.5657 [0.5651; 0.5663] 0.3 1.5 HIC
## CHINA 0.0026 [0.0026; 0.0026] 0.1 1.5 UMIC
## COLOMBIA 0.1081 [0.1080; 0.1083] 0.1 1.5 UMIC
## CROATIA 0.4368 [0.4358; 0.4379] 0.1 1.5 HIC
## CZECH REPUBLIC 1.5417 [1.5405; 1.5430] 0.4 1.5 HIC
## ECUADOR 0.3778 [0.3774; 0.3783] 0.2 1.5 UMIC
## EGYPT 0.6020 [0.6018; 0.6023] 1.5 1.5 LMIC
## ESTONIA 0.4430 [0.4412; 0.4449] 0.0 1.5 HIC
## FINLAND 4.9036 [4.9005; 4.9067] 0.7 1.5 HIC
## FRANCE 4.0148 [4.0140; 4.0156] 7.0 1.5 HIC
## GERMANY 3.0256 [3.0250; 3.0263] 6.7 1.5 HIC
## GREECE 2.3194 [2.3179; 2.3209] 0.7 1.5 HIC
## HUNGARY 1.1791 [1.1780; 1.1803] 0.3 1.5 HIC
## INDIA 0.0766 [0.0766; 0.0766] 2.7 1.5 LMIC
## IRELAND 6.5956 [6.5917; 6.5995] 0.8 1.5 HIC
## ITALY 1.9060 [1.9054; 1.9065] 3.1 1.5 HIC
## JAPAN 2.7970 [2.7965; 2.7974] 9.7 1.5 HIC
## JORDAN 0.4901 [0.4893; 0.4909] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0399 [0.0398; 0.0401] 0.0 1.5 UMIC
## KUWAIT 0.5753 [0.5740; 0.5766] 0.1 1.5 HIC
## LATVIA 0.1005 [0.0998; 0.1013] 0.0 1.5 HIC
## LEBANON 0.5807 [0.5797; 0.5817] 0.1 1.5 UMIC
## LITHUANIA 0.8691 [0.8674; 0.8709] 0.1 1.5 HIC
## LUXEMBOURG 3.5380 [3.5298; 3.5462] 0.1 1.5 HIC
## MEXICO 0.2060 [0.2059; 0.2062] 0.7 1.5 UMIC
## MOROCCO 0.0548 [0.0547; 0.0550] 0.1 1.5 LMIC
## NETHERLANDS 2.6728 [2.6715; 2.6741] 1.2 1.5 HIC
## NEW ZEALAND 0.0869 [0.0864; 0.0873] 0.0 1.5 HIC
## NORWAY 2.8620 [2.8596; 2.8645] 0.4 1.5 HIC
## PAKISTAN 0.2062 [0.2061; 0.2063] 1.1 1.5 LMIC
## PERU 0.1047 [0.1045; 0.1049] 0.1 1.5 UMIC
## PHILIPPINES 0.0678 [0.0678; 0.0679] 0.2 1.5 LMIC
## POLAND 0.0388 [0.0387; 0.0389] 0.0 1.5 HIC
## PORTUGAL 3.6144 [3.6125; 3.6163] 1.0 1.5 HIC
## PUERTO RICO 1.2472 [1.2452; 1.2491] 0.1 1.5 HIC
## ROMANIA 0.1747 [0.1744; 0.1750] 0.1 1.5 UMIC
## RUSSIA 0.9011 [0.9009; 0.9014] 3.5 1.5 UMIC
## SAUDI ARABIA 1.2680 [1.2673; 1.2687] 1.1 1.5 HIC
## SERBIA 0.3061 [0.3055; 0.3067] 0.1 1.5 UMIC
## SLOVAKIA 2.3157 [2.3136; 2.3179] 0.3 1.5 HIC
## SLOVENIA 2.6181 [2.6145; 2.6218] 0.1 1.5 HIC
## SOUTH AFRICA 0.2145 [0.2143; 0.2147] 0.3 1.5 UMIC
## SOUTH KOREA 1.2171 [1.2166; 1.2176] 1.7 1.5 HIC
## SPAIN 4.4807 [4.4797; 4.4817] 5.7 1.5 HIC
## SWEDEN 3.2563 [3.2544; 3.2582] 0.9 1.5 HIC
## SWITZERLAND 2.9188 [2.9169; 2.9207] 0.7 1.5 HIC
## TAIWAN 0.1689 [0.1687; 0.1692] 0.1 1.5 HIC
## THAILAND 0.1475 [0.1474; 0.1477] 0.3 1.5 UMIC
## TUNISIA 0.3975 [0.3969; 0.3982] 0.1 1.5 LMIC
## TÜRKIYE 1.5190 [1.5185; 1.5194] 3.2 1.5 UMIC
## UNITED ARAB EMIRATES 0.7207 [0.7198; 0.7216] 0.2 1.5 HIC
## UNITED KINGDOM 5.0509 [5.0500; 5.0518] 9.0 1.5 HIC
## UNITED STATES 2.3340 [2.3338; 2.3343] 20.2 1.5 HIC
## URUGUAY 0.9316 [0.9299; 0.9333] 0.1 1.5 HIC
## VENEZUELA 0.7561 [0.7556; 0.7566] 0.6 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.1325 [2.1324; 2.1327] 27754.66 0
## Random effects model 0.5987 [0.4682; 0.7654] -4.09 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.0216 [1.1325; 2.9345]; tau = 1.0107 [1.0642; 1.7130]
## I^2 = 100.0%; H = 4441.49
##
## Test of heterogeneity:
## Q d.f. p-value
## 1262517087.33 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 3.0394 [3.0392; 3.0395] 200279695.07 100.0%
## income = UMIC 21 0.6742 [0.6741; 0.6743] 143107710.39 100.0%
## income = LMIC 6 0.1642 [0.1642; 0.1643] 59420687.05 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 859708994.83 2 0
## Within groups 402808092.51 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.4454 [1.2512; 1.6697] 0.2058 0.4537
## income = UMIC 21 0.1786 [0.1160; 0.2749] 1.0171 1.0085
## income = LMIC 6 0.1554 [0.0650; 0.3716] 1.1875 1.0897
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 101.32 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.0163 [1.0158; 1.0168] 1.0 1.5 UMIC
## ARGENTINA 1.1057 [1.1052; 1.1062] 1.2 1.5 UMIC
## AUSTRALIA 6.8676 [6.8659; 6.8693] 4.1 1.5 HIC
## AUSTRIA 2.4571 [2.4554; 2.4588] 0.5 1.5 HIC
## BELARUS 0.0133 [0.0132; 0.0134] 0.0 1.5 UMIC
## BELGIUM 2.3686 [2.3671; 2.3700] 0.7 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0204 [0.0202; 0.0207] 0.0 1.5 UMIC
## BRAZIL 0.2552 [0.2551; 0.2553] 1.3 1.5 UMIC
## BULGARIA 0.1449 [0.1444; 0.1453] 0.0 1.5 UMIC
## CANADA 4.8298 [4.8286; 4.8310] 4.3 1.5 HIC
## CHILE 0.6778 [0.6772; 0.6785] 0.3 1.5 HIC
## CHINA 0.0034 [0.0034; 0.0034] 0.1 1.5 UMIC
## COLOMBIA 0.1500 [0.1498; 0.1502] 0.2 1.5 UMIC
## CROATIA 0.5667 [0.5655; 0.5679] 0.1 1.5 HIC
## CZECH REPUBLIC 2.0048 [2.0034; 2.0062] 0.5 1.5 HIC
## ECUADOR 0.4372 [0.4367; 0.4377] 0.2 1.5 UMIC
## EGYPT 0.8155 [0.8152; 0.8158] 1.9 1.5 LMIC
## ESTONIA 0.6044 [0.6022; 0.6066] 0.0 1.5 HIC
## FINLAND 4.7511 [4.7480; 4.7541] 0.6 1.5 HIC
## FRANCE 4.2238 [4.2230; 4.2247] 6.7 1.5 HIC
## GERMANY 3.2835 [3.2828; 3.2841] 6.6 1.5 HIC
## GREECE 2.3751 [2.3736; 2.3766] 0.6 1.5 HIC
## HUNGARY 1.1061 [1.1050; 1.1072] 0.3 1.5 HIC
## INDIA 0.0808 [0.0808; 0.0808] 2.6 1.5 LMIC
## IRELAND 8.2475 [8.2432; 8.2519] 0.9 1.5 HIC
## ITALY 1.9980 [1.9974; 1.9986] 3.0 1.5 HIC
## JAPAN 3.0553 [3.0548; 3.0558] 9.7 1.5 HIC
## JORDAN 0.5068 [0.5060; 0.5075] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0641 [0.0639; 0.0643] 0.0 1.5 UMIC
## KUWAIT 0.6329 [0.6316; 0.6342] 0.1 1.5 HIC
## LATVIA 0.0874 [0.0867; 0.0881] 0.0 1.5 HIC
## LEBANON 0.6932 [0.6922; 0.6943] 0.1 1.5 UMIC
## LITHUANIA 0.9057 [0.9039; 0.9075] 0.1 1.5 HIC
## LUXEMBOURG 3.4906 [3.4825; 3.4986] 0.0 1.5 HIC
## MEXICO 0.2250 [0.2249; 0.2252] 0.7 1.5 UMIC
## MOROCCO 0.0612 [0.0611; 0.0613] 0.1 1.5 LMIC
## NETHERLANDS 2.8345 [2.8332; 2.8359] 1.2 1.5 HIC
## NEW ZEALAND 0.1001 [0.0997; 0.1006] 0.0 1.5 HIC
## NORWAY 3.0177 [3.0153; 3.0202] 0.4 1.5 HIC
## PAKISTAN 0.2419 [0.2418; 0.2420] 1.2 1.5 LMIC
## PERU 0.1435 [0.1433; 0.1437] 0.1 1.5 UMIC
## PHILIPPINES 0.0781 [0.0780; 0.0782] 0.2 1.5 LMIC
## POLAND 0.0669 [0.0667; 0.0670] 0.1 1.5 HIC
## PORTUGAL 3.7551 [3.7532; 3.7571] 1.0 1.5 HIC
## PUERTO RICO 1.1861 [1.1842; 1.1881] 0.1 1.5 HIC
## ROMANIA 0.1684 [0.1681; 0.1687] 0.1 1.5 UMIC
## RUSSIA 0.7960 [0.7958; 0.7963] 2.8 1.5 UMIC
## SAUDI ARABIA 1.0275 [1.0269; 1.0281] 0.8 1.5 HIC
## SERBIA 0.6683 [0.6674; 0.6692] 0.1 1.5 UMIC
## SLOVAKIA 2.5507 [2.5485; 2.5529] 0.3 1.5 HIC
## SLOVENIA 2.9286 [2.9248; 2.9325] 0.1 1.5 HIC
## SOUTH AFRICA 0.2358 [0.2356; 0.2360] 0.3 1.5 UMIC
## SOUTH KOREA 1.3430 [1.3425; 1.3435] 1.7 1.5 HIC
## SPAIN 4.4954 [4.4944; 4.4964] 5.2 1.5 HIC
## SWEDEN 3.2796 [3.2777; 3.2815] 0.8 1.5 HIC
## SWITZERLAND 3.2163 [3.2143; 3.2183] 0.7 1.5 HIC
## TAIWAN 0.2086 [0.2083; 0.2089] 0.1 1.5 HIC
## THAILAND 0.1601 [0.1599; 0.1602] 0.3 1.5 UMIC
## TUNISIA 0.4702 [0.4696; 0.4709] 0.1 1.5 LMIC
## TÜRKIYE 2.0758 [2.0753; 2.0763] 4.0 1.5 UMIC
## UNITED ARAB EMIRATES 0.9940 [0.9929; 0.9951] 0.2 1.5 HIC
## UNITED KINGDOM 5.8870 [5.8860; 5.8879] 9.6 1.5 HIC
## UNITED STATES 2.4324 [2.4321; 2.4326] 19.3 1.5 HIC
## URUGUAY 1.1259 [1.1241; 1.1278] 0.1 1.5 HIC
## VENEZUELA 0.7361 [0.7356; 0.7366] 0.5 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.3234 [2.3233; 2.3235] 32424.33 0
## Random effects model 0.6843 [0.5331; 0.8783] -2.98 0.0029
##
## Quantifying heterogeneity:
## tau^2 = 1.0543 [1.1237; 2.8903]; tau = 1.0268 [1.0600; 1.7001]
## I^2 = 100.0%; H = 4743.32
##
## Test of heterogeneity:
## Q d.f. p-value
## 1439939034.97 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 3.3265 [3.3264; 3.3267] 258015807.24 100.0%
## income = UMIC 21 0.7787 [0.7786; 0.7789] 188778156.22 100.0%
## income = LMIC 6 0.2115 [0.2114; 0.2115] 91586275.41 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 901558796.10 2 0
## Within groups 538380238.87 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.6000 [1.3678; 1.8716] 0.2432 0.4932
## income = UMIC 21 0.2150 [0.1357; 0.3406] 1.1575 1.0759
## income = LMIC 6 0.1816 [0.0679; 0.4855] 1.5110 1.2292
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 80.53 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.3989 [1.3983; 1.3995] 1.3 1.5 UMIC
## ARGENTINA 1.1449 [1.1443; 1.1454] 1.1 1.5 UMIC
## AUSTRALIA 8.0026 [8.0007; 8.0044] 4.4 1.5 HIC
## AUSTRIA 3.4469 [3.4449; 3.4489] 0.7 1.5 HIC
## BELARUS 0.0210 [0.0209; 0.0212] 0.0 1.5 UMIC
## BELGIUM 3.6912 [3.6894; 3.6931] 1.0 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0389 [0.0386; 0.0393] 0.0 1.5 UMIC
## BRAZIL 0.3278 [0.3277; 0.3280] 1.5 1.5 UMIC
## BULGARIA 0.4235 [0.4227; 0.4243] 0.1 1.5 UMIC
## CANADA 5.4479 [5.4466; 5.4491] 4.5 1.5 HIC
## CHILE 0.7712 [0.7705; 0.7719] 0.3 1.5 HIC
## CHINA 0.0048 [0.0048; 0.0048] 0.2 1.5 UMIC
## COLOMBIA 0.1520 [0.1518; 0.1521] 0.2 1.5 UMIC
## CROATIA 0.9388 [0.9372; 0.9403] 0.1 1.5 HIC
## CZECH REPUBLIC 2.9635 [2.9618; 2.9652] 0.7 1.5 HIC
## ECUADOR 0.4496 [0.4491; 0.4501] 0.2 1.5 UMIC
## EGYPT 1.1842 [1.1839; 1.1846] 2.5 1.5 LMIC
## ESTONIA 0.8538 [0.8512; 0.8564] 0.0 1.5 HIC
## FINLAND 4.9233 [4.9203; 4.9264] 0.6 1.5 HIC
## FRANCE 4.3282 [4.3274; 4.3290] 6.4 1.5 HIC
## GERMANY 3.5834 [3.5827; 3.5840] 6.7 1.5 HIC
## GREECE 2.9066 [2.9049; 2.9083] 0.7 1.5 HIC
## HUNGARY 1.1861 [1.1849; 1.1872] 0.3 1.5 HIC
## INDIA 0.0826 [0.0826; 0.0827] 2.5 1.5 LMIC
## IRELAND 8.8947 [8.8902; 8.8992] 0.9 1.5 HIC
## ITALY 2.0556 [2.0550; 2.0562] 2.8 1.5 HIC
## JAPAN 3.3683 [3.3678; 3.3688] 9.8 1.5 HIC
## JORDAN 0.8144 [0.8134; 0.8153] 0.2 1.5 UMIC
## KAZAKHSTAN 0.0473 [0.0471; 0.0474] 0.0 1.5 UMIC
## KUWAIT 0.8609 [0.8594; 0.8624] 0.1 1.5 HIC
## LATVIA 0.1269 [0.1260; 0.1277] 0.0 1.5 HIC
## LEBANON 0.8859 [0.8847; 0.8870] 0.1 1.5 UMIC
## LITHUANIA 1.2196 [1.2174; 1.2217] 0.1 1.5 HIC
## LUXEMBOURG 3.6592 [3.6511; 3.6674] 0.0 1.5 HIC
## MEXICO 0.2381 [0.2379; 0.2382] 0.7 1.5 UMIC
## MOROCCO 0.0617 [0.0615; 0.0618] 0.0 1.5 LMIC
## NETHERLANDS 3.0032 [3.0018; 3.0045] 1.2 1.5 HIC
## NEW ZEALAND 0.1140 [0.1135; 0.1145] 0.0 1.5 HIC
## NORWAY 3.1358 [3.1333; 3.1384] 0.4 1.5 HIC
## PAKISTAN 0.2771 [0.2770; 0.2773] 1.3 1.5 LMIC
## PERU 0.1565 [0.1563; 0.1567] 0.1 1.5 UMIC
## PHILIPPINES 0.0931 [0.0930; 0.0932] 0.2 1.5 LMIC
## POLAND 0.3348 [0.3345; 0.3352] 0.3 1.5 HIC
## PORTUGAL 3.5745 [3.5725; 3.5764] 0.8 1.5 HIC
## PUERTO RICO 1.1569 [1.1550; 1.1589] 0.1 1.5 HIC
## ROMANIA 0.1887 [0.1883; 0.1890] 0.1 1.5 UMIC
## RUSSIA 0.1847 [0.1846; 0.1849] 0.6 1.5 UMIC
## SAUDI ARABIA 0.8962 [0.8957; 0.8968] 0.7 1.5 HIC
## SERBIA 1.0159 [1.0148; 1.0170] 0.2 1.5 UMIC
## SLOVAKIA 3.5712 [3.5686; 3.5739] 0.4 1.5 HIC
## SLOVENIA 3.3094 [3.3053; 3.3135] 0.2 1.5 HIC
## SOUTH AFRICA 0.2596 [0.2594; 0.2599] 0.3 1.5 UMIC
## SOUTH KOREA 1.5523 [1.5517; 1.5529] 1.8 1.5 HIC
## SPAIN 4.7605 [4.7595; 4.7615] 5.0 1.5 HIC
## SWEDEN 3.2710 [3.2692; 3.2729] 0.7 1.5 HIC
## SWITZERLAND 3.4599 [3.4578; 3.4619] 0.7 1.5 HIC
## TAIWAN 0.2559 [0.2555; 0.2562] 0.1 1.5 HIC
## THAILAND 0.1710 [0.1709; 0.1712] 0.3 1.5 UMIC
## TUNISIA 0.5303 [0.5296; 0.5310] 0.1 1.5 LMIC
## TÜRKIYE 2.5278 [2.5272; 2.5284] 4.6 1.5 UMIC
## UNITED ARAB EMIRATES 0.7037 [0.7028; 0.7046] 0.1 1.5 HIC
## UNITED KINGDOM 6.7927 [6.7917; 6.7938] 10.2 1.5 HIC
## UNITED STATES 2.4771 [2.4768; 2.4773] 18.2 1.5 HIC
## URUGUAY 1.1942 [1.1923; 1.1961] 0.1 1.5 HIC
## VENEZUELA 0.5555 [0.5550; 0.5559] 0.4 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.5771 [2.5770; 2.5773] 37946.55 0
## Random effects model 0.8006 [0.6206; 1.0328] -1.71 0.0870
##
## Quantifying heterogeneity:
## tau^2 = 1.0978 [1.1543; 2.9411]; tau = 1.0477 [1.0744; 1.7150]
## I^2 = 100.0%; H = 5050.88
##
## Test of heterogeneity:
## Q d.f. p-value
## 1632730148.26 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 3.6291 [3.6289; 3.6293] 325732543.59 100.0%
## income = UMIC 21 0.8607 [0.8605; 0.8608] 264750191.75 100.0%
## income = LMIC 6 0.2966 [0.2966; 0.2967] 151110460.53 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 891136952.40 2 0
## Within groups 741593195.86 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.8988 [1.6051; 2.2464] 0.2794 0.5286
## income = UMIC 21 0.2463 [0.1415; 0.4288] 1.6802 1.2962
## income = LMIC 6 0.2087 [0.0663; 0.6569] 2.0536 1.4331
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 59.62 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 2.9210 [2.9202; 2.9219] 2.5 1.5 UMIC
## ARGENTINA 1.2095 [1.2090; 1.2101] 1.1 1.5 UMIC
## AUSTRALIA 8.6417 [8.6397; 8.6436] 4.3 1.5 HIC
## AUSTRIA 3.6534 [3.6513; 3.6555] 0.7 1.5 HIC
## BELARUS 0.0391 [0.0389; 0.0393] 0.0 1.5 UMIC
## BELGIUM 4.3393 [4.3373; 4.3413] 1.0 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0747 [0.0742; 0.0752] 0.0 1.5 UMIC
## BRAZIL 0.4008 [0.4007; 0.4010] 1.7 1.5 UMIC
## BULGARIA 0.6955 [0.6945; 0.6965] 0.1 1.5 UMIC
## CANADA 6.0241 [6.0228; 6.0254] 4.5 1.5 HIC
## CHILE 0.8596 [0.8589; 0.8603] 0.3 1.5 HIC
## CHINA 0.0065 [0.0065; 0.0066] 0.2 1.5 UMIC
## COLOMBIA 0.1610 [0.1608; 0.1612] 0.2 1.5 UMIC
## CROATIA 1.1064 [1.1047; 1.1081] 0.1 1.5 HIC
## CZECH REPUBLIC 3.5544 [3.5525; 3.5563] 0.8 1.5 HIC
## ECUADOR 0.4751 [0.4746; 0.4757] 0.2 1.5 UMIC
## EGYPT 1.9074 [1.9069; 1.9078] 3.7 1.5 LMIC
## ESTONIA 1.1673 [1.1643; 1.1704] 0.0 1.5 HIC
## FINLAND 5.0831 [5.0800; 5.0862] 0.6 1.5 HIC
## FRANCE 4.4210 [4.4201; 4.4218] 5.8 1.5 HIC
## GERMANY 3.7208 [3.7201; 3.7215] 6.3 1.5 HIC
## GREECE 3.2721 [3.2703; 3.2740] 0.7 1.5 HIC
## HUNGARY 1.2721 [1.2710; 1.2733] 0.3 1.5 HIC
## INDIA 0.0859 [0.0858; 0.0859] 2.3 1.5 LMIC
## IRELAND 8.9632 [8.9587; 8.9676] 0.9 1.5 HIC
## ITALY 2.2635 [2.2629; 2.2641] 2.8 1.5 HIC
## JAPAN 3.7304 [3.7298; 3.7310] 9.7 1.5 HIC
## JORDAN 1.0108 [1.0097; 1.0118] 0.2 1.5 UMIC
## KAZAKHSTAN 0.0662 [0.0660; 0.0664] 0.0 1.5 UMIC
## KUWAIT 1.7104 [1.7083; 1.7125] 0.1 1.5 HIC
## LATVIA 0.1982 [0.1971; 0.1992] 0.0 1.5 HIC
## LEBANON 0.9871 [0.9858; 0.9883] 0.1 1.5 UMIC
## LITHUANIA 1.1829 [1.1808; 1.1850] 0.1 1.5 HIC
## LUXEMBOURG 3.8108 [3.8026; 3.8190] 0.0 1.5 HIC
## MEXICO 0.2819 [0.2817; 0.2820] 0.7 1.5 UMIC
## MOROCCO 0.1094 [0.1092; 0.1095] 0.1 1.5 LMIC
## NETHERLANDS 3.0973 [3.0959; 3.0987] 1.1 1.5 HIC
## NEW ZEALAND 0.1255 [0.1250; 0.1260] 0.0 1.5 HIC
## NORWAY 3.3743 [3.3717; 3.3769] 0.4 1.5 HIC
## PAKISTAN 0.3093 [0.3092; 0.3094] 1.3 1.5 LMIC
## PERU 0.1504 [0.1501; 0.1506] 0.1 1.5 UMIC
## PHILIPPINES 0.1064 [0.1063; 0.1065] 0.2 1.5 LMIC
## POLAND 0.7340 [0.7336; 0.7345] 0.6 1.5 HIC
## PORTUGAL 3.5756 [3.5737; 3.5776] 0.7 1.5 HIC
## PUERTO RICO 1.1721 [1.1701; 1.1741] 0.1 1.5 HIC
## ROMANIA 0.2316 [0.2312; 0.2319] 0.1 1.5 UMIC
## RUSSIA 0.2277 [0.2276; 0.2278] 0.7 1.5 UMIC
## SAUDI ARABIA 1.3068 [1.3061; 1.3074] 0.9 1.5 HIC
## SERBIA 1.3730 [1.3717; 1.3743] 0.2 1.5 UMIC
## SLOVAKIA 3.1730 [3.1705; 3.1755] 0.4 1.5 HIC
## SLOVENIA 3.4915 [3.4873; 3.4957] 0.1 1.5 HIC
## SOUTH AFRICA 0.2695 [0.2693; 0.2697] 0.3 1.5 UMIC
## SOUTH KOREA 1.8268 [1.8262; 1.8274] 1.9 1.5 HIC
## SPAIN 4.9381 [4.9371; 4.9392] 4.7 1.5 HIC
## SWEDEN 3.2632 [3.2613; 3.2650] 0.7 1.5 HIC
## SWITZERLAND 3.5588 [3.5567; 3.5609] 0.6 1.5 HIC
## TAIWAN 0.3043 [0.3040; 0.3047] 0.1 1.5 HIC
## THAILAND 0.1857 [0.1856; 0.1859] 0.3 1.5 UMIC
## TUNISIA 0.7481 [0.7473; 0.7489] 0.2 1.5 LMIC
## TÜRKIYE 3.2525 [3.2518; 3.2531] 5.4 1.5 UMIC
## UNITED ARAB EMIRATES 0.6187 [0.6179; 0.6196] 0.1 1.5 HIC
## UNITED KINGDOM 7.4479 [7.4468; 7.4490] 10.1 1.5 HIC
## UNITED STATES 2.4957 [2.4955; 2.4960] 16.5 1.5 HIC
## URUGUAY 1.3737 [1.3716; 1.3757] 0.1 1.5 HIC
## VENEZUELA 0.3986 [0.3983; 0.3990] 0.2 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.7818 [2.7817; 2.7819] 43365.34 0
## Random effects model 0.9485 [0.7393; 1.2170] -0.42 0.6778
##
## Quantifying heterogeneity:
## tau^2 = 1.0514 [1.1046; 2.7869]; tau = 1.0254 [1.0510; 1.6694]
## I^2 = 100.0%; H = 5244.10
##
## Test of heterogeneity:
## Q d.f. p-value
## 1760038568.55 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 3.8480 [3.8478; 3.8482] 360046421.19 100.0%
## income = UMIC 21 1.2266 [1.2264; 1.2267] 386768680.72 100.0%
## income = LMIC 6 0.4912 [0.4911; 0.4912] 269713335.35 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 743510131.30 2 0
## Within groups 1016528437.25 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.1440 [1.8085; 2.5417] 0.2865 0.5352
## income = UMIC 21 0.3086 [0.1714; 0.5557] 1.8908 1.3751
## income = LMIC 6 0.2759 [0.0705; 1.0796] 2.9076 1.7052
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 45.85 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 2
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 2.8536 [2.8528; 2.8544] 2.2 1.5 UMIC
## ARGENTINA 1.2643 [1.2638; 1.2649] 1.0 1.5 UMIC
## AUSTRALIA 8.7381 [8.7362; 8.7400] 4.0 1.5 HIC
## AUSTRIA 3.9874 [3.9852; 3.9895] 0.7 1.5 HIC
## BELARUS 0.0664 [0.0661; 0.0666] 0.0 1.5 UMIC
## BELGIUM 4.7493 [4.7472; 4.7514] 1.0 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1682 [0.1675; 0.1690] 0.0 1.5 UMIC
## BRAZIL 0.5116 [0.5115; 0.5118] 2.0 1.5 UMIC
## BULGARIA 0.9454 [0.9442; 0.9466] 0.1 1.5 UMIC
## CANADA 6.0709 [6.0696; 6.0722] 4.2 1.5 HIC
## CHILE 0.9066 [0.9059; 0.9073] 0.3 1.5 HIC
## CHINA 0.0108 [0.0108; 0.0108] 0.3 1.5 UMIC
## COLOMBIA 0.1810 [0.1808; 0.1812] 0.2 1.5 UMIC
## CROATIA 1.2599 [1.2581; 1.2616] 0.1 1.5 HIC
## CZECH REPUBLIC 3.9558 [3.9538; 3.9577] 0.8 1.5 HIC
## ECUADOR 0.4963 [0.4958; 0.4969] 0.2 1.5 UMIC
## EGYPT 3.7697 [3.7691; 3.7704] 6.9 1.5 LMIC
## ESTONIA 1.7170 [1.7133; 1.7207] 0.0 1.5 HIC
## FINLAND 5.1854 [5.1823; 5.1886] 0.5 1.5 HIC
## FRANCE 4.6663 [4.6654; 4.6671] 5.6 1.5 HIC
## GERMANY 3.9886 [3.9879; 3.9893] 6.1 1.5 HIC
## GREECE 3.6452 [3.6433; 3.6471] 0.7 1.5 HIC
## HUNGARY 1.3400 [1.3388; 1.3412] 0.2 1.5 HIC
## INDIA 0.0924 [0.0923; 0.0924] 2.3 1.5 LMIC
## IRELAND 7.2645 [7.2606; 7.2685] 0.6 1.5 HIC
## ITALY 2.3084 [2.3077; 2.3090] 2.6 1.5 HIC
## JAPAN 3.9854 [3.9848; 3.9859] 9.4 1.5 HIC
## JORDAN 0.3184 [0.3178; 0.3190] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0783 [0.0781; 0.0785] 0.0 1.5 UMIC
## KUWAIT 2.3085 [2.3061; 2.3110] 0.2 1.5 HIC
## LATVIA 0.2861 [0.2849; 0.2874] 0.0 1.5 HIC
## LEBANON 1.0700 [1.0688; 1.0713] 0.1 1.5 UMIC
## LITHUANIA 1.4162 [1.4139; 1.4185] 0.1 1.5 HIC
## LUXEMBOURG 3.7805 [3.7724; 3.7886] 0.0 1.5 HIC
## MEXICO 0.3123 [0.3121; 0.3124] 0.7 1.5 UMIC
## MOROCCO 0.1609 [0.1607; 0.1611] 0.1 1.5 LMIC
## NETHERLANDS 3.2409 [3.2395; 3.2423] 1.0 1.5 HIC
## NEW ZEALAND 1.0352 [1.0336; 1.0367] 0.1 1.5 HIC
## NORWAY 3.4372 [3.4346; 3.4398] 0.3 1.5 HIC
## PAKISTAN 0.3657 [0.3656; 0.3658] 1.4 1.5 LMIC
## PERU 0.1859 [0.1856; 0.1861] 0.1 1.5 UMIC
## PHILIPPINES 0.1273 [0.1272; 0.1274] 0.3 1.5 LMIC
## POLAND 1.1714 [1.1709; 1.1720] 0.8 1.5 HIC
## PORTUGAL 3.6716 [3.6697; 3.6735] 0.7 1.5 HIC
## PUERTO RICO 1.1985 [1.1965; 1.2006] 0.1 1.5 HIC
## ROMANIA 0.2448 [0.2444; 0.2451] 0.1 1.5 UMIC
## RUSSIA 0.2948 [0.2947; 0.2950] 0.8 1.5 UMIC
## SAUDI ARABIA 0.3547 [0.3544; 0.3550] 0.2 1.5 HIC
## SERBIA 1.8492 [1.8478; 1.8507] 0.3 1.5 UMIC
## SLOVAKIA 3.4508 [3.4482; 3.4534] 0.3 1.5 HIC
## SLOVENIA 3.6286 [3.6243; 3.6329] 0.1 1.5 HIC
## SOUTH AFRICA 0.3076 [0.3074; 0.3079] 0.3 1.5 UMIC
## SOUTH KOREA 2.1687 [2.1681; 2.1694] 2.1 1.5 HIC
## SPAIN 5.1063 [5.1052; 5.1073] 4.4 1.5 HIC
## SWEDEN 3.2313 [3.2294; 3.2331] 0.6 1.5 HIC
## SWITZERLAND 3.7111 [3.7090; 3.7132] 0.6 1.5 HIC
## TAIWAN 0.3825 [0.3821; 0.3829] 0.2 1.5 HIC
## THAILAND 0.2323 [0.2321; 0.2325] 0.3 1.5 UMIC
## TUNISIA 1.9419 [1.9406; 1.9433] 0.4 1.5 LMIC
## TÜRKIYE 3.5747 [3.5740; 3.5753] 5.5 1.5 UMIC
## UNITED ARAB EMIRATES 0.6074 [0.6066; 0.6082] 0.1 1.5 HIC
## UNITED KINGDOM 7.7862 [7.7851; 7.7873] 9.7 1.5 HIC
## UNITED STATES 2.5196 [2.5193; 2.5199] 15.3 1.5 HIC
## URUGUAY 1.2352 [1.2333; 1.2372] 0.1 1.5 HIC
## VENEZUELA 0.3925 [0.3921; 0.3928] 0.2 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 2.9372 [2.9371; 2.9374] 47830.30 0
## Random effects model 1.0938 [0.8546; 1.4001] 0.71 0.4763
##
## Quantifying heterogeneity:
## tau^2 = 1.0308 [1.0661; 2.6777]; tau = 1.0153 [1.0325; 1.6364]
## I^2 = 100.0%; H = 5446.99
##
## Test of heterogeneity:
## Q d.f. p-value
## 1898862125.23 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 4.0013 [4.0011; 4.0016] 368027543.06 100.0%
## income = UMIC 21 1.2599 [1.2597; 1.2600] 440123102.08 100.0%
## income = LMIC 6 1.1632 [1.1630; 1.1633] 552856709.89 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 537854770.20 2 0
## Within groups 1361007355.03 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.3691 [2.0018; 2.8037] 0.2807 0.5298
## income = UMIC 21 0.3565 [0.1975; 0.6436] 1.9075 1.3811
## income = LMIC 6 0.4144 [0.0795; 2.1606] 4.2592 2.0638
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 40.09 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2008 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.0709 [0.0708; 0.0711] 0.1 1.7 UMIC
## ARGENTINA 0.2110 [0.2108; 0.2113] 0.2 1.7 UMIC
## AUSTRALIA 1.2865 [1.2857; 1.2873] 0.7 1.7 HIC
## AUSTRIA 2.1978 [2.1961; 2.1995] 0.5 1.7 HIC
## BELARUS 0.0056 [0.0055; 0.0057] 0.0 1.7 UMIC
## BELGIUM 1.3012 [1.3001; 1.3024] 0.4 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0522 [0.0522; 0.0523] 0.3 1.7 UMIC
## BULGARIA 0.1053 [0.1049; 0.1057] 0.0 1.7 UMIC
## CANADA 4.1266 [4.1254; 4.1277] 3.7 1.7 HIC
## CHILE 0.2113 [0.2110; 0.2117] 0.1 1.7 HIC
## CHINA 0.0009 [0.0009; 0.0009] 0.0 1.7 UMIC
## COLOMBIA 0.0876 [0.0874; 0.0877] 0.1 1.7 UMIC
## CROATIA 0.3160 [0.3151; 0.3168] 0.0 1.7 HIC
## CZECH REPUBLIC 1.1416 [1.1405; 1.1427] 0.3 1.7 HIC
## ECUADOR 0.1948 [0.1944; 0.1951] 0.1 1.7 UMIC
## EGYPT 0.1064 [0.1063; 0.1066] 0.2 1.7 LMIC
## ESTONIA 0.2020 [0.2007; 0.2032] 0.0 1.7 HIC
## FINLAND 4.3558 [4.3528; 4.3587] 0.6 1.7 HIC
## FRANCE 3.5466 [3.5458; 3.5473] 5.9 1.7 HIC
## GERMANY 2.7848 [2.7842; 2.7854] 6.1 1.7 HIC
## GREECE 2.2484 [2.2470; 2.2499] 0.7 1.7 HIC
## HUNGARY 0.7213 [0.7204; 0.7221] 0.2 1.7 HIC
## INDIA 0.0537 [0.0537; 0.0537] 1.7 1.7 LMIC
## IRELAND 2.8727 [2.8700; 2.8753] 0.3 1.7 HIC
## ITALY 1.4918 [1.4913; 1.4923] 2.4 1.7 HIC
## JAPAN 0.1016 [0.1015; 0.1017] 0.4 1.7 HIC
## JORDAN 0.1265 [0.1261; 0.1270] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1175 [0.1168; 0.1182] 0.0 1.7 HIC
## LATVIA 0.4164 [0.4150; 0.4178] 0.0 1.7 HIC
## LEBANON 0.4021 [0.4012; 0.4031] 0.1 1.7 UMIC
## LITHUANIA 0.3469 [0.3459; 0.3480] 0.0 1.7 HIC
## LUXEMBOURG 3.2514 [3.2430; 3.2598] 0.0 1.7 HIC
## MEXICO 0.2788 [0.2786; 0.2789] 0.8 1.7 UMIC
## MOROCCO 0.0236 [0.0235; 0.0237] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 0.9869 [0.9853; 0.9885] 0.1 1.7 HIC
## NORWAY 3.4299 [3.4272; 3.4327] 0.4 1.7 HIC
## PAKISTAN 0.0973 [0.0972; 0.0974] 0.4 1.7 LMIC
## PERU 0.0497 [0.0495; 0.0498] 0.0 1.7 UMIC
## PHILIPPINES 0.0530 [0.0529; 0.0531] 0.1 1.7 LMIC
## POLAND 0.1513 [0.1511; 0.1515] 0.2 1.7 HIC
## PORTUGAL 3.1225 [3.1207; 3.1243] 0.9 1.7 HIC
## PUERTO RICO 5.0091 [5.0053; 5.0129] 0.5 1.7 HIC
## ROMANIA 0.3109 [0.3105; 0.3113] 0.2 1.7 UMIC
## RUSSIA 0.0289 [0.0289; 0.0290] 0.1 1.7 UMIC
## SAUDI ARABIA 0.3616 [0.3612; 0.3620] 0.3 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.4112 [1.4096; 1.4129] 0.2 1.7 HIC
## SLOVENIA 1.5309 [1.5280; 1.5337] 0.1 1.7 HIC
## SOUTH AFRICA 0.1159 [0.1158; 0.1161] 0.2 1.7 UMIC
## SOUTH KOREA 0.9479 [0.9475; 0.9484] 1.3 1.7 HIC
## SPAIN 3.8350 [3.8340; 3.8359] 4.8 1.7 HIC
## SWEDEN 3.3977 [3.3958; 3.3997] 0.8 1.7 HIC
## SWITZERLAND 1.9623 [1.9607; 1.9640] 0.4 1.7 HIC
## TAIWAN 0.1750 [0.1747; 0.1753] 0.1 1.7 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.1223 [0.1220; 0.1227] 0.0 1.7 LMIC
## TÜRKIYE 1.3869 [1.3864; 1.3873] 2.6 1.7 UMIC
## UNITED ARAB EMIRATES 0.3101 [0.3094; 0.3108] 0.1 1.7 HIC
## UNITED KINGDOM 3.0456 [3.0449; 3.0463] 5.1 1.7 HIC
## UNITED STATES 6.6631 [6.6626; 6.6636] 54.5 1.7 HIC
## URUGUAY 0.2539 [0.2530; 0.2548] 0.0 1.7 HIC
## VENEZUELA 0.6793 [0.6788; 0.6798] 0.5 1.7 UMIC
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.6284 [3.6282; 3.6286] 47464.74 0
## Random effects model 0.4114 [0.2989; 0.5663] -5.45 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.5947 [1.5270; 4.6989]; tau = 1.2628 [1.2357; 2.1677]
## I^2 = 100.0%; H = 5021.10
##
## Test of heterogeneity:
## Q d.f. p-value
## 1487474580.99 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 4.5536 [4.5533; 4.5538] 456103261.99 100.0%
## income = UMIC 17 0.5143 [0.5141; 0.5144] 111270810.72 100.0%
## income = LMIC 6 0.0634 [0.0634; 0.0635] 3125551.48 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 916974956.81 2 0
## Within groups 570499624.18 57 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.0789 [0.8447; 1.3781] 0.5769 0.7596
## income = UMIC 17 0.0961 [0.0478; 0.1932] 2.1566 1.4685
## income = LMIC 6 0.0663 [0.0475; 0.0925] 0.1728 0.4158
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 190.33 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2009 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.2753 [0.2750; 0.2756] 0.2 1.7 UMIC
## ARGENTINA 0.2916 [0.2913; 0.2918] 0.3 1.7 UMIC
## AUSTRALIA 1.4792 [1.4784; 1.4801] 0.8 1.7 HIC
## AUSTRIA 2.5392 [2.5375; 2.5410] 0.5 1.7 HIC
## BELARUS 0.0141 [0.0139; 0.0142] 0.0 1.7 UMIC
## BELGIUM 1.5782 [1.5770; 1.5795] 0.4 1.7 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.0739 [0.0739; 0.0740] 0.3 1.7 UMIC
## BULGARIA 0.2261 [0.2255; 0.2267] 0.0 1.7 UMIC
## CANADA 4.7887 [4.7875; 4.7899] 3.8 1.7 HIC
## CHILE 0.2263 [0.2259; 0.2266] 0.1 1.7 HIC
## CHINA 0.0020 [0.0020; 0.0020] 0.1 1.7 UMIC
## COLOMBIA 0.0840 [0.0839; 0.0841] 0.1 1.7 UMIC
## CROATIA 0.3611 [0.3602; 0.3621] 0.0 1.7 HIC
## CZECH REPUBLIC 1.4811 [1.4799; 1.4823] 0.4 1.7 HIC
## ECUADOR 0.2420 [0.2416; 0.2424] 0.1 1.7 UMIC
## EGYPT 0.1499 [0.1497; 0.1500] 0.3 1.7 LMIC
## ESTONIA 0.2543 [0.2529; 0.2557] 0.0 1.7 HIC
## FINLAND 4.9666 [4.9635; 4.9698] 0.6 1.7 HIC
## FRANCE 3.8551 [3.8543; 3.8559] 5.6 1.7 HIC
## GERMANY 3.2166 [3.2159; 3.2172] 6.1 1.7 HIC
## GREECE 2.6236 [2.6221; 2.6252] 0.7 1.7 HIC
## HUNGARY 0.8353 [0.8344; 0.8363] 0.2 1.7 HIC
## INDIA 0.0629 [0.0629; 0.0630] 1.8 1.7 LMIC
## IRELAND 3.5075 [3.5046; 3.5103] 0.4 1.7 HIC
## ITALY 1.6554 [1.6548; 1.6559] 2.3 1.7 HIC
## JAPAN 0.1478 [0.1477; 0.1479] 0.4 1.7 HIC
## JORDAN 0.1551 [0.1546; 0.1556] 0.0 1.7 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1514 [0.1507; 0.1522] 0.0 1.7 HIC
## LATVIA 0.5005 [0.4989; 0.5021] 0.0 1.7 HIC
## LEBANON 0.5855 [0.5844; 0.5867] 0.1 1.7 UMIC
## LITHUANIA 0.4040 [0.4028; 0.4051] 0.0 1.7 HIC
## LUXEMBOURG 3.3859 [3.3774; 3.3943] 0.0 1.7 HIC
## MEXICO 0.2677 [0.2676; 0.2679] 0.7 1.7 UMIC
## MOROCCO 0.0386 [0.0385; 0.0387] 0.0 1.7 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.1928 [1.1911; 1.1945] 0.1 1.7 HIC
## NORWAY 3.6102 [3.6074; 3.6130] 0.4 1.7 HIC
## PAKISTAN 0.1376 [0.1375; 0.1377] 0.6 1.7 LMIC
## PERU 0.0548 [0.0547; 0.0549] 0.0 1.7 UMIC
## PHILIPPINES 0.0637 [0.0636; 0.0638] 0.1 1.7 LMIC
## POLAND 0.1745 [0.1743; 0.1747] 0.2 1.7 HIC
## PORTUGAL 3.3821 [3.3803; 3.3840] 0.8 1.7 HIC
## PUERTO RICO 5.9562 [5.9520; 5.9603] 0.5 1.7 HIC
## ROMANIA 0.5838 [0.5832; 0.5843] 0.3 1.7 UMIC
## RUSSIA 0.0450 [0.0449; 0.0451] 0.2 1.7 UMIC
## SAUDI ARABIA 0.4092 [0.4088; 0.4096] 0.3 1.7 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 1.8496 [1.8477; 1.8515] 0.2 1.7 HIC
## SLOVENIA 1.7773 [1.7743; 1.7803] 0.1 1.7 HIC
## SOUTH AFRICA 0.1586 [0.1584; 0.1588] 0.2 1.7 UMIC
## SOUTH KOREA 1.1294 [1.1289; 1.1299] 1.3 1.7 HIC
## SPAIN 4.4325 [4.4315; 4.4335] 4.8 1.7 HIC
## SWEDEN 3.9503 [3.9482; 3.9524] 0.9 1.7 HIC
## SWITZERLAND 2.1416 [2.1399; 2.1433] 0.4 1.7 HIC
## TAIWAN 0.1779 [0.1776; 0.1781] 0.1 1.7 HIC
## THAILAND 0.0000 0.0 0.0 UMIC
## TUNISIA 0.2006 [0.2002; 0.2011] 0.0 1.7 LMIC
## TÜRKIYE 1.7331 [1.7326; 1.7337] 2.9 1.7 UMIC
## UNITED ARAB EMIRATES 0.4496 [0.4488; 0.4503] 0.1 1.7 HIC
## UNITED KINGDOM 3.8182 [3.8174; 3.8190] 5.6 1.7 HIC
## UNITED STATES 7.4008 [7.4003; 7.4013] 53.0 1.7 HIC
## URUGUAY 0.4441 [0.4430; 0.4453] 0.0 1.7 HIC
## VENEZUELA 0.7331 [0.7326; 0.7337] 0.5 1.7 UMIC
##
## Number of studies combined: k = 60
##
## rate 95%-CI z p-value
## Common effect model 3.9825 [3.9823; 3.9827] 54600.50 0
## Random effects model 0.5307 [0.3854; 0.7308] -3.88 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.5991 [1.4772; 4.4476]; tau = 1.2646 [1.2154; 2.1089]
## I^2 = 100.0%; H = 5451.73
##
## Test of heterogeneity:
## Q d.f. p-value
## 1753562347.73 59 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 5.0846 [5.0843; 5.0849] 508514702.66 100.0%
## income = UMIC 17 0.6088 [0.6087; 0.6089] 150350083.78 100.0%
## income = LMIC 6 0.0813 [0.0813; 0.0814] 6943865.22 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1087753696.07 2 0
## Within groups 665808651.65 57 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.2793 [1.0063; 1.6263] 0.5548 0.7448
## income = UMIC 17 0.1447 [0.0713; 0.2937] 2.2174 1.4891
## income = LMIC 6 0.0928 [0.0608; 0.1418] 0.2805 0.5296
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 128.25 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2010 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.3584 [0.3581; 0.3587] 0.3 1.6 UMIC
## ARGENTINA 0.4124 [0.4120; 0.4127] 0.3 1.6 UMIC
## AUSTRALIA 1.6897 [1.6888; 1.6906] 0.7 1.6 HIC
## AUSTRIA 3.0211 [3.0192; 3.0231] 0.5 1.6 HIC
## BELARUS 0.0180 [0.0178; 0.0181] 0.0 1.6 UMIC
## BELGIUM 2.2085 [2.2071; 2.2100] 0.5 1.6 HIC
## BOSNIA AND HERZEGOVINA 0.0000 0.0 0.0 UMIC
## BRAZIL 0.1212 [0.1211; 0.1213] 0.5 1.6 UMIC
## BULGARIA 0.3263 [0.3256; 0.3270] 0.0 1.6 UMIC
## CANADA 5.3477 [5.3464; 5.3490] 3.7 1.6 HIC
## CHILE 0.2580 [0.2576; 0.2584] 0.1 1.6 HIC
## CHINA 0.0031 [0.0031; 0.0031] 0.1 1.6 UMIC
## COLOMBIA 0.0760 [0.0759; 0.0762] 0.1 1.6 UMIC
## CROATIA 0.4543 [0.4532; 0.4553] 0.0 1.6 HIC
## CZECH REPUBLIC 1.8185 [1.8172; 1.8199] 0.4 1.6 HIC
## ECUADOR 0.2917 [0.2912; 0.2921] 0.1 1.6 UMIC
## EGYPT 0.2397 [0.2396; 0.2399] 0.4 1.6 LMIC
## ESTONIA 0.4373 [0.4355; 0.4392] 0.0 1.6 HIC
## FINLAND 5.4352 [5.4319; 5.4384] 0.6 1.6 HIC
## FRANCE 4.1757 [4.1748; 4.1765] 5.3 1.6 HIC
## GERMANY 3.5994 [3.5987; 3.6001] 5.8 1.6 HIC
## GREECE 2.8252 [2.8236; 2.8269] 0.6 1.6 HIC
## HUNGARY 1.0156 [1.0146; 1.0167] 0.2 1.6 HIC
## INDIA 0.0720 [0.0719; 0.0720] 1.8 1.6 LMIC
## IRELAND 3.9361 [3.9331; 3.9391] 0.4 1.6 HIC
## ITALY 1.8485 [1.8479; 1.8491] 2.2 1.6 HIC
## JAPAN 0.3150 [0.3149; 0.3152] 0.8 1.6 HIC
## JORDAN 0.1833 [0.1828; 0.1838] 0.0 1.6 UMIC
## KAZAKHSTAN 0.0000 0.0 0.0 UMIC
## KUWAIT 0.1562 [0.1555; 0.1569] 0.0 1.6 HIC
## LATVIA 0.5646 [0.5630; 0.5663] 0.0 1.6 HIC
## LEBANON 0.6460 [0.6448; 0.6471] 0.1 1.6 UMIC
## LITHUANIA 0.5072 [0.5059; 0.5085] 0.0 1.6 HIC
## LUXEMBOURG 3.6404 [3.6317; 3.6491] 0.0 1.6 HIC
## MEXICO 0.2720 [0.2718; 0.2721] 0.6 1.6 UMIC
## MOROCCO 0.0432 [0.0431; 0.0433] 0.0 1.6 LMIC
## NETHERLANDS 0.0000 0.0 0.0 HIC
## NEW ZEALAND 1.4364 [1.4345; 1.4383] 0.1 1.6 HIC
## NORWAY 3.9539 [3.9510; 3.9568] 0.4 1.6 HIC
## PAKISTAN 0.1573 [0.1572; 0.1574] 0.6 1.6 LMIC
## PERU 0.0700 [0.0698; 0.0702] 0.0 1.6 UMIC
## PHILIPPINES 0.0686 [0.0685; 0.0687] 0.1 1.6 LMIC
## POLAND 0.2009 [0.2007; 0.2012] 0.2 1.6 HIC
## PORTUGAL 3.8120 [3.8101; 3.8140] 0.8 1.6 HIC
## PUERTO RICO 6.7601 [6.7556; 6.7645] 0.5 1.6 HIC
## ROMANIA 0.5874 [0.5868; 0.5879] 0.2 1.6 UMIC
## RUSSIA 0.0884 [0.0883; 0.0885] 0.3 1.6 UMIC
## SAUDI ARABIA 0.4869 [0.4865; 0.4873] 0.3 1.6 HIC
## SERBIA 0.0000 0.0 0.0 UMIC
## SLOVAKIA 2.3771 [2.3750; 2.3793] 0.3 1.6 HIC
## SLOVENIA 2.0489 [2.0457; 2.0522] 0.1 1.6 HIC
## SOUTH AFRICA 0.1825 [0.1823; 0.1827] 0.2 1.6 UMIC
## SOUTH KOREA 1.2561 [1.2556; 1.2566] 1.2 1.6 HIC
## SPAIN 4.8978 [4.8967; 4.8988] 4.6 1.6 HIC
## SWEDEN 4.3114 [4.3092; 4.3136] 0.8 1.6 HIC
## SWITZERLAND 2.4739 [2.4721; 2.4758] 0.4 1.6 HIC
## TAIWAN 0.1970 [0.1967; 0.1973] 0.1 1.6 HIC
## THAILAND 0.2391 [0.2389; 0.2393] 0.3 1.6 UMIC
## TUNISIA 0.2414 [0.2409; 0.2418] 0.1 1.6 LMIC
## TÜRKIYE 2.1626 [2.1620; 2.1632] 3.1 1.6 UMIC
## UNITED ARAB EMIRATES 0.7453 [0.7443; 0.7462] 0.1 1.6 HIC
## UNITED KINGDOM 4.7452 [4.7443; 4.7461] 6.0 1.6 HIC
## UNITED STATES 8.5063 [8.5057; 8.5068] 52.6 1.6 HIC
## URUGUAY 0.5483 [0.5470; 0.5496] 0.0 1.6 HIC
## VENEZUELA 0.7677 [0.7672; 0.7682] 0.4 1.6 UMIC
##
## Number of studies combined: k = 61
##
## rate 95%-CI z p-value
## Common effect model 4.4356 [4.4354; 4.4358] 63637.75 0
## Random effects model 0.6343 [0.4586; 0.8775] -2.75 0.0060
##
## Quantifying heterogeneity:
## tau^2 = 1.6714 [1.4492; 4.2686]; tau = 1.2928 [1.2038; 2.0660]
## I^2 = 100.0%; H = 5996.98
##
## Test of heterogeneity:
## Q d.f. p-value
## 2157825551.28 60 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 37 5.7706 [5.7703; 5.7709] 618378082.54 100.0%
## income = UMIC 18 0.6753 [0.6752; 0.6755] 205401063.73 100.0%
## income = LMIC 6 0.0997 [0.0997; 0.0997] 12584477.28 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1321461927.73 2 0
## Within groups 836363623.55 58 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 37 1.5329 [1.1986; 1.9604] 0.5827 0.7634
## income = UMIC 18 0.1845 [0.0925; 0.3683] 2.2370 1.4957
## income = LMIC 6 0.1117 [0.0672; 0.1857] 0.4038 0.6355
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 101.75 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2011 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5023 [0.5019; 0.5026] 0.3 1.5 UMIC
## ARGENTINA 0.6236 [0.6232; 0.6240] 0.5 1.5 UMIC
## AUSTRALIA 1.9091 [1.9081; 1.9100] 0.8 1.5 HIC
## AUSTRIA 3.4460 [3.4440; 3.4481] 0.5 1.5 HIC
## BELARUS 0.0188 [0.0187; 0.0190] 0.0 1.5 UMIC
## BELGIUM 2.6343 [2.6327; 2.6359] 0.5 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.0490 [0.0486; 0.0494] 0.0 1.5 UMIC
## BRAZIL 0.1618 [0.1617; 0.1619] 0.6 1.5 UMIC
## BULGARIA 0.4892 [0.4884; 0.4901] 0.1 1.5 UMIC
## CANADA 5.8395 [5.8381; 5.8408] 3.6 1.5 HIC
## CHILE 0.3141 [0.3137; 0.3146] 0.1 1.5 HIC
## CHINA 0.0053 [0.0053; 0.0053] 0.1 1.5 UMIC
## COLOMBIA 0.0820 [0.0819; 0.0821] 0.1 1.5 UMIC
## CROATIA 0.4773 [0.4762; 0.4783] 0.0 1.5 HIC
## CZECH REPUBLIC 2.2432 [2.2417; 2.2447] 0.4 1.5 HIC
## ECUADOR 0.3322 [0.3317; 0.3327] 0.1 1.5 UMIC
## EGYPT 0.3250 [0.3248; 0.3252] 0.5 1.5 LMIC
## ESTONIA 0.5211 [0.5191; 0.5231] 0.0 1.5 HIC
## FINLAND 5.7890 [5.7856; 5.7923] 0.6 1.5 HIC
## FRANCE 4.3160 [4.3151; 4.3168] 4.9 1.5 HIC
## GERMANY 3.8865 [3.8858; 3.8872] 5.6 1.5 HIC
## GREECE 3.0761 [3.0744; 3.0778] 0.6 1.5 HIC
## HUNGARY 1.2037 [1.2026; 1.2048] 0.2 1.5 HIC
## INDIA 0.0808 [0.0808; 0.0808] 1.8 1.5 LMIC
## IRELAND 4.6899 [4.6866; 4.6932] 0.4 1.5 HIC
## ITALY 2.0384 [2.0378; 2.0390] 2.2 1.5 HIC
## JAPAN 1.0988 [1.0985; 1.0991] 2.5 1.5 HIC
## JORDAN 0.2052 [0.2047; 0.2057] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0285 [0.0283; 0.0286] 0.0 1.5 UMIC
## KUWAIT 0.2263 [0.2254; 0.2271] 0.0 1.5 HIC
## LATVIA 0.6788 [0.6770; 0.6807] 0.0 1.5 HIC
## LEBANON 0.7612 [0.7600; 0.7625] 0.1 1.5 UMIC
## LITHUANIA 0.7232 [0.7217; 0.7248] 0.0 1.5 HIC
## LUXEMBOURG 3.7951 [3.7863; 3.8039] 0.0 1.5 HIC
## MEXICO 0.2476 [0.2474; 0.2477] 0.5 1.5 UMIC
## MOROCCO 0.0533 [0.0531; 0.0534] 0.0 1.5 LMIC
## NETHERLANDS 2.5304 [2.5291; 2.5317] 0.8 1.5 HIC
## NEW ZEALAND 1.6623 [1.6603; 1.6643] 0.1 1.5 HIC
## NORWAY 4.4844 [4.4813; 4.4874] 0.4 1.5 HIC
## PAKISTAN 0.1948 [0.1947; 0.1949] 0.6 1.5 LMIC
## PERU 0.0813 [0.0811; 0.0815] 0.0 1.5 UMIC
## PHILIPPINES 0.0735 [0.0734; 0.0736] 0.1 1.5 LMIC
## POLAND 0.2380 [0.2377; 0.2383] 0.2 1.5 HIC
## PORTUGAL 3.8753 [3.8734; 3.8773] 0.7 1.5 HIC
## PUERTO RICO 8.4500 [8.4450; 8.4550] 0.5 1.5 HIC
## ROMANIA 0.7133 [0.7126; 0.7139] 0.3 1.5 UMIC
## RUSSIA 0.1598 [0.1597; 0.1599] 0.4 1.5 UMIC
## SAUDI ARABIA 0.5929 [0.5924; 0.5933] 0.3 1.5 HIC
## SERBIA 0.0947 [0.0944; 0.0950] 0.0 1.5 UMIC
## SLOVAKIA 2.9752 [2.9728; 2.9776] 0.3 1.5 HIC
## SLOVENIA 2.2320 [2.2286; 2.2354] 0.1 1.5 HIC
## SOUTH AFRICA 0.2093 [0.2091; 0.2095] 0.2 1.5 UMIC
## SOUTH KOREA 1.3683 [1.3677; 1.3688] 1.2 1.5 HIC
## SPAIN 5.4072 [5.4061; 5.4083] 4.6 1.5 HIC
## SWEDEN 4.6491 [4.6469; 4.6514] 0.8 1.5 HIC
## SWITZERLAND 2.7352 [2.7333; 2.7371] 0.4 1.5 HIC
## TAIWAN 0.2174 [0.2171; 0.2177] 0.1 1.5 HIC
## THAILAND 0.3580 [0.3578; 0.3583] 0.4 1.5 UMIC
## TUNISIA 0.2845 [0.2840; 0.2851] 0.1 1.5 LMIC
## TÜRKIYE 2.7223 [2.7216; 2.7229] 3.6 1.5 UMIC
## UNITED ARAB EMIRATES 0.8151 [0.8141; 0.8160] 0.1 1.5 HIC
## UNITED KINGDOM 5.6548 [5.6539; 5.6558] 6.5 1.5 HIC
## UNITED STATES 8.7465 [8.7459; 8.7470] 48.9 1.5 HIC
## URUGUAY 0.6987 [0.6973; 0.7002] 0.0 1.5 HIC
## VENEZUELA 0.8805 [0.8799; 0.8810] 0.5 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.4452 [4.4450; 4.4454] 67329.03 0
## Random effects model 0.6891 [0.5086; 0.9337] -2.40 0.0163
##
## Quantifying heterogeneity:
## tau^2 = 1.5618 [1.3732; 3.8752]; tau = 1.2497 [1.1718; 1.9686]
## I^2 = 100.0%; H = 6082.87
##
## Test of heterogeneity:
## Q d.f. p-value
## 2368087497.44 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 5.8483 [5.8481; 5.8486] 651691238.35 100.0%
## income = UMIC 21 0.8211 [0.8210; 0.8212] 276710707.56 100.0%
## income = LMIC 6 0.1217 [0.1217; 0.1217] 20640596.92 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1419044954.60 2 0
## Within groups 949042542.83 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 1.8301 [1.4526; 2.3056] 0.5278 0.7265
## income = UMIC 21 0.1880 [0.0981; 0.3603] 2.3116 1.5204
## income = LMIC 6 0.1336 [0.0745; 0.2396] 0.5323 0.7296
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 96.98 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2012 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.5436 [0.5432; 0.5440] 0.3 1.5 UMIC
## ARGENTINA 0.8075 [0.8071; 0.8080] 0.6 1.5 UMIC
## AUSTRALIA 2.1429 [2.1419; 2.1439] 0.8 1.5 HIC
## AUSTRIA 3.8892 [3.8870; 3.8914] 0.6 1.5 HIC
## BELARUS 0.0209 [0.0208; 0.0211] 0.0 1.5 UMIC
## BELGIUM 2.8197 [2.8180; 2.8213] 0.5 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1042 [0.1036; 0.1047] 0.0 1.5 UMIC
## BRAZIL 0.2003 [0.2002; 0.2004] 0.7 1.5 UMIC
## BULGARIA 0.3512 [0.3505; 0.3520] 0.0 1.5 UMIC
## CANADA 6.4490 [6.4476; 6.4504] 3.8 1.5 HIC
## CHILE 0.4029 [0.4024; 0.4033] 0.1 1.5 HIC
## CHINA 0.0100 [0.0100; 0.0101] 0.2 1.5 UMIC
## COLOMBIA 0.0904 [0.0902; 0.0905] 0.1 1.5 UMIC
## CROATIA 0.5290 [0.5279; 0.5302] 0.0 1.5 HIC
## CZECH REPUBLIC 2.4462 [2.4447; 2.4478] 0.4 1.5 HIC
## ECUADOR 0.3715 [0.3710; 0.3720] 0.1 1.5 UMIC
## EGYPT 0.4519 [0.4517; 0.4522] 0.7 1.5 LMIC
## ESTONIA 0.6499 [0.6477; 0.6522] 0.0 1.5 HIC
## FINLAND 6.2340 [6.2305; 6.2375] 0.6 1.5 HIC
## FRANCE 4.6399 [4.6391; 4.6408] 5.0 1.5 HIC
## GERMANY 4.1898 [4.1890; 4.1905] 5.7 1.5 HIC
## GREECE 2.9461 [2.9444; 2.9478] 0.5 1.5 HIC
## HUNGARY 1.3818 [1.3806; 1.3831] 0.2 1.5 HIC
## INDIA 0.0843 [0.0843; 0.0844] 1.8 1.5 LMIC
## IRELAND 5.7487 [5.7451; 5.7523] 0.4 1.5 HIC
## ITALY 2.1488 [2.1482; 2.1494] 2.2 1.5 HIC
## JAPAN 1.7794 [1.7790; 1.7798] 3.8 1.5 HIC
## JORDAN 0.2711 [0.2705; 0.2717] 0.0 1.5 UMIC
## KAZAKHSTAN 0.0437 [0.0436; 0.0439] 0.0 1.5 UMIC
## KUWAIT 0.3421 [0.3411; 0.3431] 0.0 1.5 HIC
## LATVIA 0.8875 [0.8853; 0.8896] 0.0 1.5 HIC
## LEBANON 0.7933 [0.7921; 0.7945] 0.1 1.5 UMIC
## LITHUANIA 0.8457 [0.8440; 0.8474] 0.0 1.5 HIC
## LUXEMBOURG 4.0584 [4.0494; 4.0674] 0.0 1.5 HIC
## MEXICO 0.2474 [0.2473; 0.2476] 0.5 1.5 UMIC
## MOROCCO 0.0653 [0.0651; 0.0654] 0.0 1.5 LMIC
## NETHERLANDS 2.8475 [2.8461; 2.8488] 0.8 1.5 HIC
## NEW ZEALAND 1.9467 [1.9446; 1.9489] 0.1 1.5 HIC
## NORWAY 4.8179 [4.8147; 4.8211] 0.4 1.5 HIC
## PAKISTAN 0.2124 [0.2123; 0.2125] 0.7 1.5 LMIC
## PERU 0.0954 [0.0952; 0.0955] 0.0 1.5 UMIC
## PHILIPPINES 0.0780 [0.0779; 0.0781] 0.1 1.5 LMIC
## POLAND 0.2180 [0.2177; 0.2182] 0.1 1.5 HIC
## PORTUGAL 4.3551 [4.3530; 4.3572] 0.8 1.5 HIC
## PUERTO RICO 9.4847 [9.4794; 9.4900] 0.6 1.5 HIC
## ROMANIA 0.7460 [0.7453; 0.7466] 0.3 1.5 UMIC
## RUSSIA 0.3209 [0.3208; 0.3211] 0.8 1.5 UMIC
## SAUDI ARABIA 0.7378 [0.7373; 0.7383] 0.4 1.5 HIC
## SERBIA 0.1341 [0.1337; 0.1345] 0.0 1.5 UMIC
## SLOVAKIA 3.2284 [3.2259; 3.2309] 0.3 1.5 HIC
## SLOVENIA 2.4452 [2.4416; 2.4487] 0.1 1.5 HIC
## SOUTH AFRICA 0.2379 [0.2377; 0.2381] 0.2 1.5 UMIC
## SOUTH KOREA 1.7305 [1.7299; 1.7311] 1.5 1.5 HIC
## SPAIN 5.7247 [5.7236; 5.7258] 4.5 1.5 HIC
## SWEDEN 4.7299 [4.7276; 4.7322] 0.8 1.5 HIC
## SWITZERLAND 2.9603 [2.9584; 2.9623] 0.4 1.5 HIC
## TAIWAN 0.2224 [0.2221; 0.2227] 0.1 1.5 HIC
## THAILAND 0.4123 [0.4121; 0.4126] 0.5 1.5 UMIC
## TUNISIA 0.4017 [0.4011; 0.4024] 0.1 1.5 LMIC
## TÜRKIYE 3.1476 [3.1470; 3.1483] 4.0 1.5 UMIC
## UNITED ARAB EMIRATES 0.3576 [0.3569; 0.3582] 0.1 1.5 HIC
## UNITED KINGDOM 6.6897 [6.6887; 6.6908] 7.3 1.5 HIC
## UNITED STATES 8.4560 [8.4555; 8.4565] 44.7 1.5 HIC
## URUGUAY 1.0938 [1.0920; 1.0957] 0.1 1.5 HIC
## VENEZUELA 1.0497 [1.0491; 1.0503] 0.5 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.3669 [4.3667; 4.3671] 68686.77 0
## Random effects model 0.7995 [0.5972; 1.0702] -1.50 0.1326
##
## Quantifying heterogeneity:
## tau^2 = 1.4395 [1.3196; 3.6551]; tau = 1.1998 [1.1488; 1.9118]
## I^2 = 100.0%; H = 6176.17
##
## Test of heterogeneity:
## Q d.f. p-value
## 2441281374.51 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 5.8356 [5.8353; 5.8359] 576470883.83 100.0%
## income = UMIC 21 0.9058 [0.9057; 0.9060] 345088843.44 100.0%
## income = LMIC 6 0.1448 [0.1448; 0.1449] 35222923.64 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1484498723.60 2 0
## Within groups 956782650.91 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.0373 [1.6575; 2.5042] 0.4211 0.6489
## income = UMIC 21 0.2331 [0.1216; 0.4468] 2.3135 1.5210
## income = LMIC 6 0.1597 [0.0794; 0.3209] 0.7613 0.8725
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 79.02 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2013 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.6123 [0.6119; 0.6128] 0.3 1.5 UMIC
## ARGENTINA 0.9655 [0.9650; 0.9660] 0.6 1.5 UMIC
## AUSTRALIA 3.9071 [3.9058; 3.9084] 1.3 1.5 HIC
## AUSTRIA 4.2035 [4.2012; 4.2058] 0.5 1.5 HIC
## BELARUS 0.0344 [0.0342; 0.0346] 0.0 1.5 UMIC
## BELGIUM 2.8712 [2.8696; 2.8729] 0.5 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1485 [0.1479; 0.1492] 0.0 1.5 UMIC
## BRAZIL 0.2464 [0.2463; 0.2465] 0.7 1.5 UMIC
## BULGARIA 0.4812 [0.4804; 0.4820] 0.1 1.5 UMIC
## CANADA 7.3987 [7.3972; 7.4002] 3.8 1.5 HIC
## CHILE 0.4888 [0.4882; 0.4893] 0.1 1.5 HIC
## CHINA 0.0143 [0.0143; 0.0143] 0.3 1.5 UMIC
## COLOMBIA 0.1060 [0.1058; 0.1062] 0.1 1.5 UMIC
## CROATIA 0.5611 [0.5599; 0.5622] 0.0 1.5 HIC
## CZECH REPUBLIC 2.8554 [2.8537; 2.8570] 0.4 1.5 HIC
## ECUADOR 0.3806 [0.3801; 0.3811] 0.1 1.5 UMIC
## EGYPT 0.5793 [0.5791; 0.5796] 0.7 1.5 LMIC
## ESTONIA 0.8479 [0.8453; 0.8505] 0.0 1.5 HIC
## FINLAND 6.7770 [6.7733; 6.7806] 0.5 1.5 HIC
## FRANCE 4.9101 [4.9092; 4.9110] 4.6 1.5 HIC
## GERMANY 4.4645 [4.4637; 4.4653] 5.3 1.5 HIC
## GREECE 3.2543 [3.2525; 3.2561] 0.5 1.5 HIC
## HUNGARY 1.6067 [1.6054; 1.6080] 0.2 1.5 HIC
## INDIA 0.0874 [0.0874; 0.0874] 1.6 1.5 LMIC
## IRELAND 6.9816 [6.9776; 6.9855] 0.5 1.5 HIC
## ITALY 2.2892 [2.2886; 2.2899] 2.0 1.5 HIC
## JAPAN 2.3035 [2.3031; 2.3040] 4.3 1.5 HIC
## JORDAN 0.4185 [0.4177; 0.4192] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0488 [0.0487; 0.0490] 0.0 1.5 UMIC
## KUWAIT 0.7306 [0.7291; 0.7321] 0.0 1.5 HIC
## LATVIA 1.1425 [1.1401; 1.1449] 0.0 1.5 HIC
## LEBANON 0.8309 [0.8297; 0.8321] 0.1 1.5 UMIC
## LITHUANIA 1.0202 [1.0183; 1.0221] 0.0 1.5 HIC
## LUXEMBOURG 4.0818 [4.0729; 4.0907] 0.0 1.5 HIC
## MEXICO 0.2527 [0.2525; 0.2528] 0.4 1.5 UMIC
## MOROCCO 0.0781 [0.0780; 0.0783] 0.0 1.5 LMIC
## NETHERLANDS 3.0818 [3.0804; 3.0832] 0.8 1.5 HIC
## NEW ZEALAND 2.2133 [2.2110; 2.2155] 0.1 1.5 HIC
## NORWAY 4.9931 [4.9899; 4.9963] 0.4 1.5 HIC
## PAKISTAN 0.2292 [0.2291; 0.2293] 0.6 1.5 LMIC
## PERU 0.1101 [0.1099; 0.1103] 0.0 1.5 UMIC
## PHILIPPINES 0.0914 [0.0913; 0.0915] 0.1 1.5 LMIC
## POLAND 0.2820 [0.2817; 0.2822] 0.2 1.5 HIC
## PORTUGAL 4.3602 [4.3581; 4.3623] 0.7 1.5 HIC
## PUERTO RICO 9.6517 [9.6463; 9.6571] 0.5 1.5 HIC
## ROMANIA 0.8871 [0.8865; 0.8878] 0.3 1.5 UMIC
## RUSSIA 0.7291 [0.7288; 0.7293] 1.5 1.5 UMIC
## SAUDI ARABIA 1.0581 [1.0575; 1.0587] 0.5 1.5 HIC
## SERBIA 0.2477 [0.2471; 0.2482] 0.0 1.5 UMIC
## SLOVAKIA 3.4486 [3.4460; 3.4511] 0.3 1.5 HIC
## SLOVENIA 2.6081 [2.6045; 2.6118] 0.1 1.5 HIC
## SOUTH AFRICA 0.2529 [0.2527; 0.2532] 0.2 1.5 UMIC
## SOUTH KOREA 1.8725 [1.8719; 1.8731] 1.4 1.5 HIC
## SPAIN 6.0784 [6.0772; 6.0795] 4.2 1.5 HIC
## SWEDEN 5.1810 [5.1786; 5.1834] 0.7 1.5 HIC
## SWITZERLAND 3.1810 [3.1789; 3.1830] 0.4 1.5 HIC
## TAIWAN 0.2939 [0.2935; 0.2942] 0.1 1.5 HIC
## THAILAND 0.5597 [0.5594; 0.5600] 0.6 1.5 UMIC
## TUNISIA 0.4665 [0.4658; 0.4671] 0.1 1.5 LMIC
## TÜRKIYE 3.5727 [3.5720; 3.5734] 4.0 1.5 UMIC
## UNITED ARAB EMIRATES 0.5663 [0.5655; 0.5671] 0.1 1.5 HIC
## UNITED KINGDOM 8.0419 [8.0408; 8.0431] 7.6 1.5 HIC
## UNITED STATES 9.5835 [9.5830; 9.5841] 44.2 1.5 HIC
## URUGUAY 1.1789 [1.1770; 1.1809] 0.1 1.5 HIC
## VENEZUELA 1.2948 [1.2941; 1.2954] 0.6 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 4.8683 [4.8681; 4.8685] 79244.65 0
## Random effects model 0.9651 [0.7220; 1.2901] -0.24 0.8104
##
## Quantifying heterogeneity:
## tau^2 = 1.4252 [1.2649; 3.4746]; tau = 1.1938 [1.1247; 1.8640]
## I^2 = 100.0%; H = 6622.16
##
## Test of heterogeneity:
## Q d.f. p-value
## 2806590083.64 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 6.5760 [6.5757; 6.5763] 659063858.68 100.0%
## income = UMIC 21 1.0495 [1.0493; 1.0496] 393503431.21 100.0%
## income = LMIC 6 0.1692 [0.1691; 0.1692] 51399203.72 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1702623590.03 2 0
## Within groups 1103966493.61 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.3930 [1.9469; 2.9413] 0.4210 0.6489
## income = UMIC 21 0.2997 [0.1637; 0.5485] 1.9974 1.4133
## income = LMIC 6 0.1839 [0.0840; 0.4023] 0.9577 0.9786
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 73.20 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2014 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 0.8777 [ 0.8772; 0.8782] 0.4 1.5 UMIC
## ARGENTINA 1.0803 [ 1.0798; 1.0808] 0.6 1.5 UMIC
## AUSTRALIA 6.1076 [ 6.1060; 6.1093] 1.9 1.5 HIC
## AUSTRIA 4.5047 [ 4.5024; 4.5071] 0.5 1.5 HIC
## BELARUS 0.0451 [ 0.0449; 0.0453] 0.0 1.5 UMIC
## BELGIUM 3.1127 [ 3.1110; 3.1144] 0.5 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.1600 [ 0.1593; 0.1607] 0.0 1.5 UMIC
## BRAZIL 0.2972 [ 0.2971; 0.2973] 0.8 1.5 UMIC
## BULGARIA 0.5914 [ 0.5905; 0.5924] 0.1 1.5 UMIC
## CANADA 8.1048 [ 8.1032; 8.1063] 3.8 1.5 HIC
## CHILE 0.5859 [ 0.5853; 0.5865] 0.1 1.5 HIC
## CHINA 0.0183 [ 0.0183; 0.0184] 0.3 1.5 UMIC
## COLOMBIA 0.1232 [ 0.1230; 0.1233] 0.1 1.5 UMIC
## CROATIA 0.6205 [ 0.6193; 0.6218] 0.0 1.5 HIC
## CZECH REPUBLIC 3.2558 [ 3.2540; 3.2576] 0.4 1.5 HIC
## ECUADOR 0.4213 [ 0.4207; 0.4218] 0.1 1.5 UMIC
## EGYPT 0.7263 [ 0.7261; 0.7266] 0.9 1.5 LMIC
## ESTONIA 1.0802 [ 1.0772; 1.0831] 0.0 1.5 HIC
## FINLAND 6.5386 [ 6.5351; 6.5422] 0.5 1.5 HIC
## FRANCE 5.2787 [ 5.2778; 5.2797] 4.4 1.5 HIC
## GERMANY 4.7110 [ 4.7102; 4.7117] 5.0 1.5 HIC
## GREECE 3.2833 [ 3.2815; 3.2851] 0.5 1.5 HIC
## HUNGARY 1.7894 [ 1.7880; 1.7908] 0.2 1.5 HIC
## INDIA 0.0949 [ 0.0949; 0.0949] 1.6 1.5 LMIC
## IRELAND 7.5993 [ 7.5952; 7.6035] 0.5 1.5 HIC
## ITALY 2.4579 [ 2.4572; 2.4585] 1.9 1.5 HIC
## JAPAN 2.9007 [ 2.9002; 2.9012] 4.8 1.5 HIC
## JORDAN 0.5784 [ 0.5776; 0.5793] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0671 [ 0.0669; 0.0673] 0.0 1.5 UMIC
## KUWAIT 0.6781 [ 0.6767; 0.6794] 0.0 1.5 HIC
## LATVIA 1.3962 [ 1.3935; 1.3989] 0.0 1.5 HIC
## LEBANON 0.9179 [ 0.9167; 0.9192] 0.1 1.5 UMIC
## LITHUANIA 1.2046 [ 1.2026; 1.2067] 0.0 1.5 HIC
## LUXEMBOURG 4.2040 [ 4.1951; 4.2129] 0.0 1.5 HIC
## MEXICO 0.2758 [ 0.2756; 0.2759] 0.4 1.5 UMIC
## MOROCCO 0.0859 [ 0.0858; 0.0861] 0.0 1.5 LMIC
## NETHERLANDS 3.3712 [ 3.3698; 3.3727] 0.7 1.5 HIC
## NEW ZEALAND 2.5248 [ 2.5224; 2.5272] 0.2 1.5 HIC
## NORWAY 5.0716 [ 5.0683; 5.0748] 0.3 1.5 HIC
## PAKISTAN 0.2485 [ 0.2483; 0.2486] 0.6 1.5 LMIC
## PERU 0.1205 [ 0.1203; 0.1207] 0.0 1.5 UMIC
## PHILIPPINES 0.0950 [ 0.0949; 0.0951] 0.1 1.5 LMIC
## POLAND 0.3720 [ 0.3717; 0.3723] 0.2 1.5 HIC
## PORTUGAL 4.8798 [ 4.8776; 4.8821] 0.7 1.5 HIC
## PUERTO RICO 11.3900 [11.3841; 11.3959] 0.5 1.5 HIC
## ROMANIA 1.0166 [ 1.0159; 1.0174] 0.3 1.5 UMIC
## RUSSIA 0.9373 [ 0.9370; 0.9376] 1.8 1.5 UMIC
## SAUDI ARABIA 1.3930 [ 1.3923; 1.3936] 0.6 1.5 HIC
## SERBIA 0.3969 [ 0.3962; 0.3976] 0.0 1.5 UMIC
## SLOVAKIA 3.7363 [ 3.7336; 3.7390] 0.3 1.5 HIC
## SLOVENIA 2.9582 [ 2.9543; 2.9621] 0.1 1.5 HIC
## SOUTH AFRICA 0.2727 [ 0.2724; 0.2729] 0.2 1.5 UMIC
## SOUTH KOREA 2.0337 [ 2.0331; 2.0344] 1.3 1.5 HIC
## SPAIN 6.3670 [ 6.3658; 6.3682] 3.9 1.5 HIC
## SWEDEN 5.6087 [ 5.6063; 5.6112] 0.7 1.5 HIC
## SWITZERLAND 3.4305 [ 3.4284; 3.4326] 0.4 1.5 HIC
## TAIWAN 0.3606 [ 0.3602; 0.3610] 0.1 1.5 HIC
## THAILAND 0.6334 [ 0.6331; 0.6337] 0.6 1.5 UMIC
## TUNISIA 0.5206 [ 0.5199; 0.5213] 0.1 1.5 LMIC
## TÜRKIYE 4.0912 [ 4.0905; 4.0920] 4.1 1.5 UMIC
## UNITED ARAB EMIRATES 0.8103 [ 0.8094; 0.8113] 0.1 1.5 HIC
## UNITED KINGDOM 9.5420 [ 9.5407; 9.5432] 8.1 1.5 HIC
## UNITED STATES 10.2551 [10.2546; 10.2557] 42.6 1.5 HIC
## URUGUAY 1.3733 [ 1.3712; 1.3753] 0.1 1.5 HIC
## VENEZUELA 1.5927 [ 1.5919; 1.5934] 0.6 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.2538 [5.2536; 5.2540] 87794.11 0
## Random effects model 1.1159 [0.8385; 1.4850] 0.75 0.4521
##
## Quantifying heterogeneity:
## tau^2 = 1.3821 [1.2326; 3.3581]; tau = 1.1756 [1.1102; 1.8325]
## I^2 = 100.0%; H = 6947.47
##
## Test of heterogeneity:
## Q d.f. p-value
## 3089108590.44 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 7.1419 [7.1416; 7.1422] 697065631.26 100.0%
## income = UMIC 21 1.2179 [1.2177; 1.2180] 461153450.94 100.0%
## income = LMIC 6 0.1996 [0.1996; 0.1996] 70314896.82 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 1860574611.42 2 0
## Within groups 1228533979.02 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 2.7130 [2.2212; 3.3138] 0.3958 0.6291
## income = UMIC 21 0.3631 [0.2003; 0.6584] 1.9353 1.3912
## income = LMIC 6 0.2044 [0.0871; 0.4793] 1.1351 1.0654
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 68.00 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2015 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.0435 [ 1.0430; 1.0440] 0.5 1.5 UMIC
## ARGENTINA 1.2359 [ 1.2353; 1.2364] 0.6 1.5 UMIC
## AUSTRALIA 7.5188 [ 7.5170; 7.5207] 2.1 1.5 HIC
## AUSTRIA 4.6332 [ 4.6308; 4.6355] 0.5 1.5 HIC
## BELARUS 0.0609 [ 0.0607; 0.0612] 0.0 1.5 UMIC
## BELGIUM 3.4623 [ 3.4605; 3.4641] 0.5 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.2186 [ 0.2178; 0.2194] 0.0 1.5 UMIC
## BRAZIL 0.3671 [ 0.3669; 0.3672] 0.9 1.5 UMIC
## BULGARIA 0.6868 [ 0.6858; 0.6878] 0.1 1.5 UMIC
## CANADA 8.9837 [ 8.9821; 8.9854] 3.8 1.5 HIC
## CHILE 0.6971 [ 0.6964; 0.6977] 0.1 1.5 HIC
## CHINA 0.0236 [ 0.0236; 0.0236] 0.4 1.5 UMIC
## COLOMBIA 0.1650 [ 0.1648; 0.1652] 0.1 1.5 UMIC
## CROATIA 0.7200 [ 0.7187; 0.7214] 0.0 1.5 HIC
## CZECH REPUBLIC 3.8896 [ 3.8876; 3.8915] 0.5 1.5 HIC
## ECUADOR 0.4820 [ 0.4815; 0.4826] 0.1 1.5 UMIC
## EGYPT 0.9495 [ 0.9492; 0.9498] 1.0 1.5 LMIC
## ESTONIA 1.3609 [ 1.3576; 1.3642] 0.0 1.5 HIC
## FINLAND 6.6345 [ 6.6310; 6.6381] 0.4 1.5 HIC
## FRANCE 5.5188 [ 5.5179; 5.5198] 4.2 1.5 HIC
## GERMANY 4.9624 [ 4.9616; 4.9632] 4.7 1.5 HIC
## GREECE 3.3903 [ 3.3884; 3.3921] 0.4 1.5 HIC
## HUNGARY 1.7720 [ 1.7706; 1.7734] 0.2 1.5 HIC
## INDIA 0.1004 [ 0.1004; 0.1005] 1.5 1.5 LMIC
## IRELAND 9.3143 [ 9.3098; 9.3189] 0.5 1.5 HIC
## ITALY 2.5593 [ 2.5587; 2.5600] 1.8 1.5 HIC
## JAPAN 3.1521 [ 3.1516; 3.1526] 4.7 1.5 HIC
## JORDAN 0.5796 [ 0.5788; 0.5804] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0902 [ 0.0899; 0.0904] 0.0 1.5 UMIC
## KUWAIT 0.7833 [ 0.7818; 0.7847] 0.0 1.5 HIC
## LATVIA 1.6802 [ 1.6772; 1.6832] 0.0 1.5 HIC
## LEBANON 1.0485 [ 1.0472; 1.0498] 0.1 1.5 UMIC
## LITHUANIA 1.2834 [ 1.2812; 1.2855] 0.0 1.5 HIC
## LUXEMBOURG 4.1852 [ 4.1764; 4.1940] 0.0 1.5 HIC
## MEXICO 0.2998 [ 0.2996; 0.2999] 0.4 1.5 UMIC
## MOROCCO 0.1033 [ 0.1032; 0.1035] 0.0 1.5 LMIC
## NETHERLANDS 3.5567 [ 3.5552; 3.5582] 0.7 1.5 HIC
## NEW ZEALAND 2.9494 [ 2.9468; 2.9520] 0.2 1.5 HIC
## NORWAY 5.6805 [ 5.6771; 5.6839] 0.3 1.5 HIC
## PAKISTAN 0.2817 [ 0.2816; 0.2818] 0.7 1.5 LMIC
## PERU 0.1993 [ 0.1991; 0.1996] 0.1 1.5 UMIC
## PHILIPPINES 0.1056 [ 0.1055; 0.1057] 0.1 1.5 LMIC
## POLAND 0.4692 [ 0.4688; 0.4696] 0.2 1.5 HIC
## PORTUGAL 5.0680 [ 5.0657; 5.0703] 0.6 1.5 HIC
## PUERTO RICO 12.9824 [12.9761; 12.9888] 0.5 1.5 HIC
## ROMANIA 1.1237 [ 1.1229; 1.1244] 0.3 1.5 UMIC
## RUSSIA 0.8413 [ 0.8411; 0.8416] 1.4 1.5 UMIC
## SAUDI ARABIA 1.2320 [ 1.2313; 1.2326] 0.5 1.5 HIC
## SERBIA 0.7818 [ 0.7808; 0.7827] 0.1 1.5 UMIC
## SLOVAKIA 4.1169 [ 4.1141; 4.1198] 0.3 1.5 HIC
## SLOVENIA 3.2621 [ 3.2581; 3.2662] 0.1 1.5 HIC
## SOUTH AFRICA 0.2909 [ 0.2907; 0.2912] 0.2 1.5 UMIC
## SOUTH KOREA 2.1810 [ 2.1803; 2.1817] 1.3 1.5 HIC
## SPAIN 6.4617 [ 6.4605; 6.4629] 3.5 1.5 HIC
## SWEDEN 6.0197 [ 6.0172; 6.0223] 0.7 1.5 HIC
## SWITZERLAND 3.7244 [ 3.7222; 3.7265] 0.4 1.5 HIC
## TAIWAN 0.4099 [ 0.4094; 0.4103] 0.1 1.5 HIC
## THAILAND 0.6688 [ 0.6685; 0.6691] 0.5 1.5 UMIC
## TUNISIA 0.5660 [ 0.5653; 0.5668] 0.1 1.5 LMIC
## TÜRKIYE 4.6908 [ 4.6900; 4.6915] 4.3 1.5 UMIC
## UNITED ARAB EMIRATES 1.0947 [ 1.0936; 1.0958] 0.1 1.5 HIC
## UNITED KINGDOM 11.0046 [11.0033; 11.0059] 8.5 1.5 HIC
## UNITED STATES 11.6350 [11.6343; 11.6356] 43.6 1.5 HIC
## URUGUAY 1.4897 [ 1.4876; 1.4919] 0.1 1.5 HIC
## VENEZUELA 1.0882 [ 1.0875; 1.0888] 0.4 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 5.9048 [5.9046; 5.9050] 99352.31 0
## Random effects model 1.2656 [0.9445; 1.6958] 1.58 0.1147
##
## Quantifying heterogeneity:
## tau^2 = 1.4489 [1.2407; 3.3792]; tau = 1.2037 [1.1139; 1.8383]
## I^2 = 100.0%; H = 7483.56
##
## Test of heterogeneity:
## Q d.f. p-value
## 3584233012.20 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 8.0522 [8.0519; 8.0525] 839037339.89 100.0%
## income = UMIC 21 1.3106 [1.3105; 1.3108] 551920284.09 100.0%
## income = LMIC 6 0.2471 [0.2471; 0.2472] 102827393.89 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2090447994.32 2 0
## Within groups 1493785017.87 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 3.0013 [2.4353; 3.6989] 0.4320 0.6573
## income = UMIC 21 0.4294 [0.2300; 0.8019] 2.1318 1.4601
## income = LMIC 6 0.2344 [0.0908; 0.6049] 1.4037 1.1848
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 56.18 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2016 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 1.4666 [ 1.4659; 1.4672] 0.6 1.5 UMIC
## ARGENTINA 1.2701 [ 1.2696; 1.2707] 0.6 1.5 UMIC
## AUSTRALIA 8.6392 [ 8.6372; 8.6411] 2.2 1.5 HIC
## AUSTRIA 5.5558 [ 5.5533; 5.5584] 0.5 1.5 HIC
## BELARUS 0.0745 [ 0.0742; 0.0748] 0.0 1.5 UMIC
## BELGIUM 4.6625 [ 4.6604; 4.6645] 0.6 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.3015 [ 0.3006; 0.3025] 0.0 1.5 UMIC
## BRAZIL 0.4401 [ 0.4399; 0.4402] 1.0 1.5 UMIC
## BULGARIA 0.9792 [ 0.9780; 0.9804] 0.1 1.5 UMIC
## CANADA 9.9954 [ 9.9937; 9.9971] 3.9 1.5 HIC
## CHILE 0.7907 [ 0.7900; 0.7914] 0.2 1.5 HIC
## CHINA 0.0286 [ 0.0286; 0.0286] 0.4 1.5 UMIC
## COLOMBIA 0.1667 [ 0.1665; 0.1669] 0.1 1.5 UMIC
## CROATIA 1.0827 [ 1.0810; 1.0843] 0.0 1.5 HIC
## CZECH REPUBLIC 4.9366 [ 4.9344; 4.9388] 0.6 1.5 HIC
## ECUADOR 0.4980 [ 0.4975; 0.4986] 0.1 1.5 UMIC
## EGYPT 1.3411 [ 1.3407; 1.3414] 1.3 1.5 LMIC
## ESTONIA 1.7473 [ 1.7435; 1.7510] 0.0 1.5 HIC
## FINLAND 7.1434 [ 7.1397; 7.1470] 0.4 1.5 HIC
## FRANCE 5.6502 [ 5.6492; 5.6511] 3.9 1.5 HIC
## GERMANY 5.2710 [ 5.2702; 5.2718] 4.6 1.5 HIC
## GREECE 3.9338 [ 3.9318; 3.9358] 0.4 1.5 HIC
## HUNGARY 1.9009 [ 1.8995; 1.9024] 0.2 1.5 HIC
## INDIA 0.1038 [ 0.1038; 0.1038] 1.5 1.5 LMIC
## IRELAND 10.0262 [10.0215; 10.0310] 0.5 1.5 HIC
## ITALY 2.6128 [ 2.6122; 2.6135] 1.7 1.5 HIC
## JAPAN 3.4593 [ 3.4587; 3.4598] 4.7 1.5 HIC
## JORDAN 0.8999 [ 0.8989; 0.9009] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0727 [ 0.0725; 0.0729] 0.0 1.5 UMIC
## KUWAIT 1.3133 [ 1.3114; 1.3152] 0.1 1.5 HIC
## LATVIA 1.8527 [ 1.8495; 1.8558] 0.0 1.5 HIC
## LEBANON 1.3224 [ 1.3210; 1.3239] 0.1 1.5 UMIC
## LITHUANIA 1.6490 [ 1.6465; 1.6514] 0.1 1.5 HIC
## LUXEMBOURG 4.3268 [ 4.3179; 4.3357] 0.0 1.5 HIC
## MEXICO 0.3259 [ 0.3257; 0.3260] 0.4 1.5 UMIC
## MOROCCO 0.1130 [ 0.1129; 0.1132] 0.0 1.5 LMIC
## NETHERLANDS 3.7671 [ 3.7656; 3.7687] 0.7 1.5 HIC
## NEW ZEALAND 3.4626 [ 3.4598; 3.4654] 0.2 1.5 HIC
## NORWAY 5.8838 [ 5.8804; 5.8872] 0.3 1.5 HIC
## PAKISTAN 0.3136 [ 0.3135; 0.3137] 0.7 1.5 LMIC
## PERU 0.2131 [ 0.2128; 0.2134] 0.1 1.5 UMIC
## PHILIPPINES 0.1206 [ 0.1205; 0.1207] 0.1 1.5 LMIC
## POLAND 0.7797 [ 0.7792; 0.7801] 0.3 1.5 HIC
## PORTUGAL 4.9211 [ 4.9189; 4.9233] 0.5 1.5 HIC
## PUERTO RICO 14.3848 [14.3780; 14.3916] 0.5 1.5 HIC
## ROMANIA 1.3004 [ 1.2996; 1.3013] 0.3 1.5 UMIC
## RUSSIA 0.2778 [ 0.2776; 0.2779] 0.4 1.5 UMIC
## SAUDI ARABIA 1.3045 [ 1.3039; 1.3052] 0.5 1.5 HIC
## SERBIA 1.1215 [ 1.1203; 1.1226] 0.1 1.5 UMIC
## SLOVAKIA 5.0693 [ 5.0662; 5.0724] 0.3 1.5 HIC
## SLOVENIA 3.6342 [ 3.6299; 3.6385] 0.1 1.5 HIC
## SOUTH AFRICA 0.3238 [ 0.3236; 0.3241] 0.2 1.5 UMIC
## SOUTH KOREA 2.4374 [ 2.4367; 2.4381] 1.3 1.5 HIC
## SPAIN 6.8250 [ 6.8237; 6.8262] 3.4 1.5 HIC
## SWEDEN 6.6761 [ 6.6734; 6.6787] 0.7 1.5 HIC
## SWITZERLAND 3.9619 [ 3.9597; 3.9641] 0.4 1.5 HIC
## TAIWAN 0.4897 [ 0.4893; 0.4902] 0.1 1.5 HIC
## THAILAND 0.7037 [ 0.7034; 0.7040] 0.5 1.5 UMIC
## TUNISIA 0.6241 [ 0.6234; 0.6249] 0.1 1.5 LMIC
## TÜRKIYE 5.0234 [ 5.0226; 5.0242] 4.3 1.5 UMIC
## UNITED ARAB EMIRATES 0.7763 [ 0.7753; 0.7772] 0.1 1.5 HIC
## UNITED KINGDOM 12.5535 [12.5521; 12.5549] 8.9 1.5 HIC
## UNITED STATES 12.7740 [12.7734; 12.7747] 43.9 1.5 HIC
## URUGUAY 1.5550 [ 1.5528; 1.5572] 0.1 1.5 HIC
## VENEZUELA 0.6046 [ 0.6041; 0.6051] 0.2 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 6.5643 [6.5641; 6.5645] 110256.30 0
## Random effects model 1.4086 [1.0497; 1.8904] 2.28 0.0224
##
## Quantifying heterogeneity:
## tau^2 = 1.4640 [1.2644; 3.4394]; tau = 1.2100 [1.1245; 1.8546]
## I^2 = 100.0%; H = 7862.69
##
## Test of heterogeneity:
## Q d.f. p-value
## 3956604647.10 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 8.8598 [8.8594; 8.8601] 951641538.31 100.0%
## income = UMIC 21 1.3875 [1.3874; 1.3877] 637775673.31 100.0%
## income = LMIC 6 0.3328 [0.3327; 0.3329] 165525751.56 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2201661683.92 2 0
## Within groups 1754942963.18 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 3.4099 [2.7590; 4.2144] 0.4438 0.6662
## income = UMIC 21 0.4570 [0.2317; 0.9012] 2.5209 1.5877
## income = LMIC 6 0.2681 [0.0893; 0.8053] 1.8893 1.3745
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 47.85 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2017 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 3.0485 [ 3.0476; 3.0494] 1.2 1.5 UMIC
## ARGENTINA 1.3337 [ 1.3331; 1.3342] 0.6 1.5 UMIC
## AUSTRALIA 9.3000 [ 9.2980; 9.3020] 2.2 1.5 HIC
## AUSTRIA 5.7414 [ 5.7388; 5.7440] 0.5 1.5 HIC
## BELARUS 0.1234 [ 0.1230; 0.1238] 0.0 1.5 UMIC
## BELGIUM 5.2455 [ 5.2433; 5.2477] 0.6 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.3756 [ 0.3746; 0.3767] 0.0 1.5 UMIC
## BRAZIL 0.5157 [ 0.5155; 0.5158] 1.0 1.5 UMIC
## BULGARIA 1.2179 [ 1.2166; 1.2192] 0.1 1.5 UMIC
## CANADA 10.7934 [10.7916; 10.7951] 3.8 1.5 HIC
## CHILE 0.8795 [ 0.8788; 0.8802] 0.2 1.5 HIC
## CHINA 0.0358 [ 0.0358; 0.0358] 0.5 1.5 UMIC
## COLOMBIA 0.1728 [ 0.1727; 0.1730] 0.1 1.5 UMIC
## CROATIA 1.2346 [ 1.2328; 1.2364] 0.0 1.5 HIC
## CZECH REPUBLIC 5.6135 [ 5.6112; 5.6159] 0.6 1.5 HIC
## ECUADOR 0.5272 [ 0.5266; 0.5277] 0.1 1.5 UMIC
## EGYPT 2.0563 [ 2.0558; 2.0568] 1.9 1.5 LMIC
## ESTONIA 2.2727 [ 2.2684; 2.2769] 0.0 1.5 HIC
## FINLAND 7.7485 [ 7.7446; 7.7523] 0.4 1.5 HIC
## FRANCE 5.7629 [ 5.7619; 5.7639] 3.6 1.5 HIC
## GERMANY 5.3977 [ 5.3969; 5.3985] 4.3 1.5 HIC
## GREECE 4.2748 [ 4.2727; 4.2768] 0.4 1.5 HIC
## HUNGARY 2.0198 [ 2.0184; 2.0213] 0.2 1.5 HIC
## INDIA 0.1090 [ 0.1090; 0.1090] 1.4 1.5 LMIC
## IRELAND 10.1551 [10.1504; 10.1599] 0.5 1.5 HIC
## ITALY 2.8330 [ 2.8323; 2.8337] 1.6 1.5 HIC
## JAPAN 3.8160 [ 3.8155; 3.8166] 4.7 1.5 HIC
## JORDAN 1.1300 [ 1.1289; 1.1311] 0.1 1.5 UMIC
## KAZAKHSTAN 0.0892 [ 0.0890; 0.0894] 0.0 1.5 UMIC
## KUWAIT 2.6181 [ 2.6155; 2.6207] 0.1 1.5 HIC
## LATVIA 2.1990 [ 2.1955; 2.2024] 0.0 1.5 HIC
## LEBANON 1.4841 [ 1.4826; 1.4856] 0.1 1.5 UMIC
## LITHUANIA 1.7040 [ 1.7015; 1.7065] 0.0 1.5 HIC
## LUXEMBOURG 4.4402 [ 4.4314; 4.4491] 0.0 1.5 HIC
## MEXICO 0.3797 [ 0.3795; 0.3799] 0.5 1.5 UMIC
## MOROCCO 0.1463 [ 0.1461; 0.1465] 0.0 1.5 LMIC
## NETHERLANDS 3.8860 [ 3.8844; 3.8875] 0.6 1.5 HIC
## NEW ZEALAND 3.8784 [ 3.8754; 3.8813] 0.2 1.5 HIC
## NORWAY 6.3251 [ 6.3215; 6.3286] 0.3 1.5 HIC
## PAKISTAN 0.3403 [ 0.3402; 0.3405] 0.7 1.5 LMIC
## PERU 0.2237 [ 0.2234; 0.2239] 0.1 1.5 UMIC
## PHILIPPINES 0.1306 [ 0.1305; 0.1307] 0.1 1.5 LMIC
## POLAND 1.2022 [ 1.2016; 1.2028] 0.4 1.5 HIC
## PORTUGAL 4.9841 [ 4.9818; 4.9863] 0.5 1.5 HIC
## PUERTO RICO 16.0002 [15.9929; 16.0075] 0.5 1.5 HIC
## ROMANIA 1.5396 [ 1.5387; 1.5405] 0.3 1.5 UMIC
## RUSSIA 0.3809 [ 0.3807; 0.3810] 0.5 1.5 UMIC
## SAUDI ARABIA 1.8736 [ 1.8729; 1.8744] 0.6 1.5 HIC
## SERBIA 1.4789 [ 1.4775; 1.4802] 0.1 1.5 UMIC
## SLOVAKIA 4.7357 [ 4.7327; 4.7387] 0.2 1.5 HIC
## SLOVENIA 3.8317 [ 3.8273; 3.8361] 0.1 1.5 HIC
## SOUTH AFRICA 0.3350 [ 0.3347; 0.3352] 0.2 1.5 UMIC
## SOUTH KOREA 2.7612 [ 2.7604; 2.7619] 1.4 1.5 HIC
## SPAIN 7.1036 [ 7.1024; 7.1049] 3.2 1.5 HIC
## SWEDEN 7.1681 [ 7.1654; 7.1709] 0.7 1.5 HIC
## SWITZERLAND 4.0655 [ 4.0633; 4.0678] 0.3 1.5 HIC
## TAIWAN 0.5447 [ 0.5442; 0.5452] 0.1 1.5 HIC
## THAILAND 0.8093 [ 0.8090; 0.8097] 0.5 1.5 UMIC
## TUNISIA 0.8396 [ 0.8388; 0.8405] 0.1 1.5 LMIC
## TÜRKIYE 5.8298 [ 5.8289; 5.8307] 4.5 1.5 UMIC
## UNITED ARAB EMIRATES 0.6780 [ 0.6771; 0.6789] 0.1 1.5 HIC
## UNITED KINGDOM 13.5562 [13.5548; 13.5577] 8.7 1.5 HIC
## UNITED STATES 13.9471 [13.9465; 13.9478] 43.5 1.5 HIC
## URUGUAY 1.7113 [ 1.7090; 1.7135] 0.1 1.5 HIC
## VENEZUELA 0.4621 [ 0.4617; 0.4625] 0.1 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 7.0395 [7.0393; 7.0398] 120460.32 0
## Random effects model 1.6148 [1.2059; 2.1623] 3.22 0.0013
##
## Quantifying heterogeneity:
## tau^2 = 1.4424 [1.2156; 3.2813]; tau = 1.2010 [1.1026; 1.8114]
## I^2 = 100.0%; H = 8243.80
##
## Test of heterogeneity:
## Q d.f. p-value
## 4349451471.67 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 9.5747 [9.5744; 9.5751] 1058810343.14 100.0%
## income = UMIC 21 1.7098 [1.7096; 1.7100] 775835855.84 100.0%
## income = LMIC 6 0.5126 [0.5126; 0.5127] 285073308.29 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2229731964.39 2 0
## Within groups 2119719507.27 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 3.7805 [3.0502; 4.6856] 0.4557 0.6751
## income = UMIC 21 0.5467 [0.2799; 1.0676] 2.4489 1.5649
## income = LMIC 6 0.3271 [0.0890; 1.2022] 2.6467 1.6269
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 40.45 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
## [1] 2018 3
## rate 95%-CI %W(common) %W(random) income
## ALGERIA 2.9435 [ 2.9427; 2.9444] 1.1 1.5 UMIC
## ARGENTINA 1.3902 [ 1.3896; 1.3907] 0.6 1.5 UMIC
## AUSTRALIA 9.4384 [ 9.4364; 9.4404] 2.1 1.5 HIC
## AUSTRIA 6.0504 [ 6.0478; 6.0531] 0.5 1.5 HIC
## BELARUS 0.1868 [ 0.1863; 0.1872] 0.0 1.5 UMIC
## BELGIUM 5.6263 [ 5.6240; 5.6286] 0.6 1.5 HIC
## BOSNIA AND HERZEGOVINA 0.5333 [ 0.5320; 0.5346] 0.0 1.5 UMIC
## BRAZIL 0.6318 [ 0.6316; 0.6320] 1.2 1.5 UMIC
## BULGARIA 1.3771 [ 1.3757; 1.3785] 0.1 1.5 UMIC
## CANADA 10.8953 [10.8936; 10.8971] 3.7 1.5 HIC
## CHILE 0.9280 [ 0.9273; 0.9288] 0.2 1.5 HIC
## CHINA 0.0438 [ 0.0438; 0.0438] 0.6 1.5 UMIC
## COLOMBIA 0.1955 [ 0.1953; 0.1957] 0.1 1.5 UMIC
## CROATIA 1.3816 [ 1.3797; 1.3834] 0.1 1.5 HIC
## CZECH REPUBLIC 6.0546 [ 6.0522; 6.0571] 0.6 1.5 HIC
## ECUADOR 0.5475 [ 0.5469; 0.5481] 0.1 1.5 UMIC
## EGYPT 3.9344 [ 3.9338; 3.9351] 3.5 1.5 LMIC
## ESTONIA 3.0000 [ 2.9952; 3.0049] 0.0 1.5 HIC
## FINLAND 8.2826 [ 8.2786; 8.2865] 0.4 1.5 HIC
## FRANCE 6.0179 [ 6.0169; 6.0188] 3.5 1.5 HIC
## GERMANY 5.6636 [ 5.6628; 5.6645] 4.3 1.5 HIC
## GREECE 4.6340 [ 4.6318; 4.6361] 0.4 1.5 HIC
## HUNGARY 2.1507 [ 2.1492; 2.1522] 0.2 1.5 HIC
## INDIA 0.1168 [ 0.1168; 0.1168] 1.4 1.5 LMIC
## IRELAND 8.5190 [ 8.5147; 8.5234] 0.4 1.5 HIC
## ITALY 2.8818 [ 2.8811; 2.8825] 1.6 1.5 HIC
## JAPAN 4.0686 [ 4.0680; 4.0692] 4.7 1.5 HIC
## JORDAN 0.6513 [ 0.6504; 0.6521] 0.1 1.5 UMIC
## KAZAKHSTAN 0.1047 [ 0.1044; 0.1049] 0.0 1.5 UMIC
## KUWAIT 3.2250 [ 3.2222; 3.2279] 0.1 1.5 HIC
## LATVIA 2.6394 [ 2.6357; 2.6432] 0.0 1.5 HIC
## LEBANON 1.5687 [ 1.5672; 1.5703] 0.1 1.5 UMIC
## LITHUANIA 2.0118 [ 2.0091; 2.0146] 0.1 1.5 HIC
## LUXEMBOURG 4.4238 [ 4.4151; 4.4326] 0.0 1.5 HIC
## MEXICO 0.4124 [ 0.4122; 0.4126] 0.5 1.5 UMIC
## MOROCCO 0.2011 [ 0.2009; 0.2014] 0.1 1.5 LMIC
## NETHERLANDS 4.0538 [ 4.0522; 4.0554] 0.6 1.5 HIC
## NEW ZEALAND 5.0261 [ 5.0228; 5.0295] 0.2 1.5 HIC
## NORWAY 6.6320 [ 6.6284; 6.6356] 0.3 1.5 HIC
## PAKISTAN 0.3985 [ 0.3984; 0.3986] 0.8 1.5 LMIC
## PERU 0.2562 [ 0.2559; 0.2565] 0.1 1.5 UMIC
## PHILIPPINES 0.1572 [ 0.1570; 0.1573] 0.2 1.5 LMIC
## POLAND 1.6797 [ 1.6790; 1.6803] 0.6 1.5 HIC
## PORTUGAL 5.1522 [ 5.1499; 5.1545] 0.5 1.5 HIC
## PUERTO RICO 15.1233 [15.1160; 15.1305] 0.4 1.5 HIC
## ROMANIA 1.6365 [ 1.6356; 1.6375] 0.3 1.5 UMIC
## RUSSIA 0.5050 [ 0.5048; 0.5052] 0.7 1.5 UMIC
## SAUDI ARABIA 1.1357 [ 1.1351; 1.1363] 0.3 1.5 HIC
## SERBIA 1.9559 [ 1.9543; 1.9574] 0.2 1.5 UMIC
## SLOVAKIA 5.1626 [ 5.1594; 5.1657] 0.3 1.5 HIC
## SLOVENIA 3.9999 [ 3.9954; 4.0044] 0.1 1.5 HIC
## SOUTH AFRICA 0.3738 [ 0.3736; 0.3741] 0.2 1.5 UMIC
## SOUTH KOREA 3.0995 [ 3.0987; 3.1003] 1.4 1.5 HIC
## SPAIN 7.3282 [ 7.3269; 7.3295] 3.1 1.5 HIC
## SWEDEN 7.6667 [ 7.6638; 7.6695] 0.7 1.5 HIC
## SWITZERLAND 4.2259 [ 4.2237; 4.2282] 0.3 1.5 HIC
## TAIWAN 0.6414 [ 0.6408; 0.6419] 0.1 1.5 HIC
## THAILAND 0.9387 [ 0.9383; 0.9390] 0.6 1.5 UMIC
## TUNISIA 2.0328 [ 2.0314; 2.0342] 0.2 1.5 LMIC
## TÜRKIYE 6.0922 [ 6.0913; 6.0931] 4.5 1.5 UMIC
## UNITED ARAB EMIRATES 0.6640 [ 0.6631; 0.6648] 0.1 1.5 HIC
## UNITED KINGDOM 13.8879 [13.8864; 13.8894] 8.4 1.5 HIC
## UNITED STATES 14.2069 [14.2062; 14.2076] 42.0 1.5 HIC
## URUGUAY 1.5402 [ 1.5380; 1.5424] 0.0 1.5 HIC
## VENEZUELA 0.4182 [ 0.4178; 0.4186] 0.1 1.5 UMIC
##
## Number of studies combined: k = 65
##
## rate 95%-CI z p-value
## Common effect model 7.0838 [7.0836; 7.0840] 124461.49 0
## Random effects model 1.7840 [1.3391; 2.3767] 3.95 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 1.3924 [1.1595; 3.1026]; tau = 1.1800 [1.0768; 1.7614]
## I^2 = 100.0%; H = 8406.45
##
## Test of heterogeneity:
## Q d.f. p-value
## 4522777067.76 64 0
##
## Results for subgroups (common effect model):
## k rate 95%-CI Q I^2
## income = HIC 38 9.7768 [9.7765; 9.7771] 1054363705.55 100.0%
## income = UMIC 21 1.7263 [1.7261; 1.7264] 822572064.85 100.0%
## income = LMIC 6 1.1377 [1.1376; 1.1379] 585568586.11 100.0%
##
## Test for subgroup differences (common effect model):
## Q d.f. p-value
## Between groups 2060272711.24 2 0
## Within groups 2462504356.51 62 0
##
## Results for subgroups (random effects model):
## k rate 95%-CI tau^2 tau
## income = HIC 38 3.9898 [3.2334; 4.9231] 0.4371 0.6611
## income = UMIC 21 0.6061 [0.3151; 1.1657] 2.3383 1.5291
## income = LMIC 6 0.4769 [0.0975; 2.3332] 3.9370 1.9842
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 34.60 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
## - Jackson method for confidence interval of tau^2 and tau
## - Log transformation
##income
gaba_income<-as.data.frame(do.call(rbind, datout))[c(5:10)]
gaba_income$Year<-as.numeric(gaba_income$Year)
out <- unlist(gaba_income)
Year<-out[1:33]
Drug<-out[34:66]
DDDTID<-out[67:165]
lower<-out[166:264]
upper<-out[265:363]
out2<-data.frame(Year,Drug)
out3<-do.call("rbind", replicate(3, out2, simplify = FALSE))
newdata <- out3%>% arrange(Drug, Year)
newdata$DDDTID<-DDDTID
newdata$lower<-lower
newdata$upper<-upper
region_name<-(rep(c("HIC","UMIC","LMIC"),33))
newdata$Income<-region_name
newdata$Drug<-c(rep(("Gabapentin"), 33),
rep(("Pregabalin"), 33),
rep(("Gabapentinoids"), 33))
gaba_income2<-newdata
gaba_income2$`DDD/TID`<-gaba_income2$DDDTID
gaba_income2$`DDD/TID - lower`<-gaba_income2$lower
gaba_income2$`DDD/TID - upper`<-gaba_income2$upper
gaba_income3<-gaba_income2[c(1,2,6:9)]
library(DT)
datatable(gaba_income3, options = list(
autoWidth = TRUE,
columnDefs = list(list(width = '100px', targets = c(1, 3)))
))
write.csv(gaba_income3,"D:/R/midas gaba/R1/Output_16_Sensitivity_3_Subgroup_AAPC_income_meta.csv")
